{"metadata":{"bundle_type":"episode_pack","bundle_version":"prompt24_v1","workspace_slug":"orbital","episode_id":"a350eb1a-9f36-413b-9e55-b3e20671e382","exported_at":"2026-08-31T23:57:15.059112Z"},"summary":{"content_asset_count":21,"transcript_segment_count":200,"asset_types":{"content_calendar_item":3,"newsletter_summary":1,"social_post":3,"hook":3,"quote_card":4,"clip_candidate":4,"episode_theme":3},"ranked_theme_ids":[],"theme_snapshot_ids":[]},"episode":{"id":"a350eb1a-9f36-413b-9e55-b3e20671e382","source_id":"59e59180-06c1-4427-b41a-6c5d25af3226","source_slug":"yt-HxrOOPPdbD4-d9654309","transcript_document_id":"a0007a3a-3861-4fee-ba9e-d048a9589e62","raw_asset_id":"c2d09eb4-96cc-47ab-9a20-21046ef1f463","title":"You SUCK at Prompting AI For Research (Do This instead)","description":"▶ Become a Master Academic Writer With AI using my course: https://academy.academiainsider.com/courses/ai-writing-course ▶Join 21,000+ email subscribers receiving the free tools and academic tips directly from me: https://academiainsider.com/newsletter/ You could be losing up to 30% of your output quality just from prompting Claude the wrong way. In this Claude prompting guide I walk through Anthropic's own official prompt engineering best practices — and translate every one of them into what actually matters for academia and research. If you use Claude AI for research papers, literature reviews, abstracts, peer review responses or academic writing, these are the five prompting rules that will immediately improve every response you get. Most people asking \"why is Claude's output so bad?\" are not using a bad model. They are using bad prompt structure. Fix the structure and the same model gives you dramatically better academic writing, cleaner synthesis and far fewer hallucinated cita","external_url":"https://www.youtube.com/watch?v=HxrOOPPdbD4","status":"published","published_at":"2026-08-25T02:30:29Z","transcript_segment_count":200,"content_asset_count":21,"details_json":{"file_name":null,"published_at":"2026-08-25T02:30:29+00:00","transcript_format":"youtube_captions"},"latest_transcript_segments":[{"id":"137a618d-07af-4f58-85bf-45a382135227","segment_index":0,"speaker_name":null,"start_seconds":null,"end_seconds":null,"text":"30% is the amount of quality you can"},{"id":"331a83b5-7b40-4676-a38d-9b82ca5191a5","segment_index":1,"speaker_name":null,"start_seconds":null,"end_seconds":null,"text":"lose if you are prompting the wrong way"},{"id":"ddfd8cd9-4e61-49d6-80dc-f8b07fb4969d","segment_index":2,"speaker_name":null,"start_seconds":null,"end_seconds":null,"text":"in Claude. So, in this video, I'm going"},{"id":"26b4861c-0742-4846-a37a-4a3f64cb9db0","segment_index":3,"speaker_name":null,"start_seconds":null,"end_seconds":null,"text":"to share with you the actual real"},{"id":"722afe74-dc1b-4666-9376-b1032afb6d62","segment_index":4,"speaker_name":null,"start_seconds":null,"end_seconds":null,"text":"prompting guide from Anthropic"},{"id":"bef9d05f-20c2-474b-88a9-8a396c72b2c8","segment_index":5,"speaker_name":null,"start_seconds":null,"end_seconds":null,"text":"themselves, and this is how you optimize"},{"id":"ae6813aa-cd6d-44d0-b1ec-e0b68be59db4","segment_index":6,"speaker_name":null,"start_seconds":null,"end_seconds":null,"text":"for academia and research. Now, I've"},{"id":"21968eb5-d813-4f7b-a8f0-a02f4466d505","segment_index":7,"speaker_name":null,"start_seconds":null,"end_seconds":null,"text":"taken all of the information from the"}],"content_asset_counts":{"content_calendar_item":3,"newsletter_summary":1,"social_post":3,"hook":3,"quote_card":4,"clip_candidate":4,"episode_theme":3}},"source":{"id":"59e59180-06c1-4427-b41a-6c5d25af3226","workspace_id":"d9654309-c206-4820-9522-1886720e58c4","name":"You SUCK at Prompting AI For Research (Do This instead)","slug":"yt-HxrOOPPdbD4-d9654309","source_type":"youtube_video","enabled":true,"base_url":"https://www.youtube.com/watch?v=HxrOOPPdbD4","feed_url":null,"domain":"youtube.com","jurisdiction":null,"authority_tier":"tier_d","source_class":"exploratory","access_posture":"fully_fetchable","discovery_confidence":null,"original_discovery_confidence":null,"approval_basis":null,"promotion_path":null,"discovery_context_json":{},"content_type_label":null,"cadence":null,"fetch_method":null,"last_ingested_at":"2026-08-27T06:23:23.843515Z","archived_at":null,"last_run_status":"succeeded","document_count":1,"recent_document_count":1,"policy":{"layer":null,"fetch_strategy":null,"effective_cadence":"weekly","is_event_driven":false,"last_checked_at":"2026-08-27T06:23:23.843515Z","last_changed_at":"2026-08-27T06:23:23.843515Z","next_check_at":"2026-09-03T06:23:23.843515Z","change_state":null,"cache_etag":null,"cache_last_modified":null,"cache_status":null,"stale":false,"due_now":false,"due_reason":"within_cadence"},"evidence_posture":{"origin_lane":"search_council_exploratory","source_class":"exploratory","trust_posture":"exploratory","evidence_class":"source_evidence","access_posture":"fully_fetchable","promotion_status":"candidate","admissibility_status":"context_only","evidence_floor_status":"supporting_floor","evidence_floor_reason":"Exploratory and promoted material remains below the primary evidence floor until it earns stronger trust.","summary":"Exploratory and promoted material stays below the primary evidence floor.","reasons":["Explicit access posture hint: fully fetchable.","This source is still exploratory and has not cleared the promotion floor.","Exploratory sources remain context-only until they are promoted through discovery.","Exploratory and promoted material remains below the primary evidence floor until it earns stronger trust."]},"source_reliability":{"score":42.6,"band":"guarded","summary":"Source reliability is guarded at 42.6/100.","reasons":["Authority tier is tier_d, contributing to a guarded reliability posture.","Access/admissibility posture is context_only, so Orbital scores reliability with that trust ceiling in mind.","The source has 1 documents in Orbital, including 1 in the recent window.","This source came through discovery/promotion, so reliability is intentionally capped below a strong curated source unless history accumulates."],"factors":[{"name":"authority_tier","value":7.0,"reason":"Higher authority tiers carry more baseline reliability."},{"name":"source_class","value":4.0,"reason":"Curated and manually approved sources start from a stronger trust base than exploratory promotions."},{"name":"admissibility","value":4.0,"reason":"Access and admissibility posture should raise or limit downstream reliance."},{"name":"coverage_history","value":2.6,"reason":"Sources with more durable document history are more reliable than one-off appearances."},{"name":"operational_health","value":6.0,"reason":"Recent ingestion success is a bounded proxy for source stability."},{"name":"overclaim_risk","value":0.0,"reason":"Low-access or lightly observed sources should be scored more cautiously."}]},"lifecycle":{},"config_json":{"title":"You SUCK at Prompting AI For Research (Do This instead)","video_id":"HxrOOPPdbD4","channel_id":"UCFqXmQ56-Gp1rIKa-GoAJvQ","fetched_at":"2026-08-27T06:19:33.650841+00:00","description":"▶ Become a Master Academic Writer With AI using my course: https://academy.academiainsider.com/courses/ai-writing-course\n▶Join 21,000+ email subscribers receiving the free tools and academic tips directly from me: \nhttps://academiainsider.com/newsletter/\n\nYou could be losing up to 30% of your output quality just from prompting Claude the wrong way. In this Claude prompting guide I walk through Anthropic's own official prompt engineering best practices — and translate every one of them into what actually matters for academia and research. If you use Claude AI for research papers, literature reviews, abstracts, peer review responses or academic writing, these are the five prompting rules that will immediately improve every response you get.\n\nMost people asking \"why is Claude's output so bad?\" are not using a bad model. They are using bad prompt structure. Fix the structure and the same model gives you dramatically better academic writing, cleaner synthesis and far fewer hallucinated cita","topic_seeds":["Academia Where Fewer"],"caption_kind":"asr_auto","channel_name":"Andy Stapleton","published_at":"2026-08-25T02:30:29Z","source_universe":{"cadence":"weekly","warnings":[],"feed_urls":[],"source_type":"youtube_video","sitemap_urls":[],"allowed_paths":[],"blocked_paths":[],"crawl_posture":"manual_approval","domain_family":"youtube.com","freshness_sla":"weekly","source_family":"video_media","trust_posture":"community","authority_tier":"tier_d","branch_relevance":[],"historical_yield":{},"rejection_history":[],"source_family_raw":"video_media","audience_relevance":[],"contamination_history":[],"robots_unavailable_policy":"fail_closed"},"transcript_text":"30% is the amount of quality you can\nlose if you are prompting the wrong way\nin Claude. So, in this video, I'm going\nto share with you the actual real\nprompting guide from Anthropic\nthemselves, and this is how you optimize\nfor academia and research. Now, I've\ntaken all of the information from the\nprompting best practices and actually\nhad a look at them from the lens of\nacademia and research, and these are the\nthings that you can do right now to\nimprove all of the\nresponses you get. A lot of times people\ncome to me and they say like, \"Oh, it's\nnot very good.\" And that's because they\nare prompting the wrong way in a lot of\nways. So, here are the five things you\ncan do. The first one is if you have a\ndocument, make sure it's at the top and\nnot the bottom. A lot of the times we\ngo, \"Do this, do this, do this.\" And\nthen give the document underneath. Now,\nI know you can actually add documents um\nseparately as files now, but if you're\npasting and copying text, this is what\nyou should know is that the official\nprompt thing docs say that you should\nput Here, I'll put a laser on. There we\nare, that's better, isn't it? So, you\nshould put long-form data at the top.\nSo, place your long document near the\ntop of your prompt above your query\ninstructions and any examples. This\nimproves performance across all of the\nAnthropic models, and surprisingly, it\nwill probably do the same thing or\nunsurprisingly, it will probably do the\nsame thing across other um large\nlanguage models as well like Chat GPT,\nGemini, and those sort of things. So,\nqueries at the end can improve response\nquality by 30% in tests. And this is\nimportant because in academia, we are\ndealing with documents a lot. So, this\nis what a lot of us want to do is put\nthe question and then copy and paste the\npaper underneath. But instead, what you\nneed to do is put the paper or any\ncontext that you want, and then your\nquestion at the end. Now, this is very\nimportant if your text is over 20,000\ntokens. What does that look like? Well,\nyou can use a calculator. I actually\nlike using this one from Open AI, and I\nput in one of these papers, which is\njust like a typical length paper in my\nfield, and you can see that a typical\npaper is approximately 10,000 tokens.\nSo, if your papers are longer, you could\neasily run into the 20,000 token limit\nfor this sort of prompting practice. So,\nI highly recommend that if you are\nputting in more than one paper, that you\nalways put them first because otherwise,\nall of the details will get lost because\nof the token lengths and limits. That is\nan important thing. So, the next thing\nyou should know about is if you are\nasking for information from a document,\nyou need to ask it for quotes first.\nThis is so important in academia and\nresearch because we all the time want to\nknow the actual information, and this\nreduces hallucination. So, this is what\nthe official prompting docs say, \"Ground\nresponses in quotes.\" And this is what\nyou can ask it for. And then, for long\ndocument task, which is what we're doing\na lot in academia, \"Ask Claude to quote\nrelevant parts of the document first\nbefore carrying out its its task.\" This\nhelps sort of like anchor Claude and\nother large language models in the facts\nthat it finds rather than trying to\nhallucinate them later. So, for example,\nthis is the anti-hallucination prompt\nthat you can use, \"Before answering,\nextract verbatim quotes from the\ndocuments that bear on the question,\neach tagged with its original source.\"\nThat's so important. And then, answer\nusing only these quotes. So, if you are\nlooking for information from a document,\nmake sure you do this because it will\nreduce your hallucination rate. That is\nso very important. All right then, these\nnext ones are just as important. So, a\nlot of times when we're asking Claude to\ndo something, we often say, \"Don't do a\nthing.\" But it actually responds far\nbetter to positive reinforcement. Don't\nwe all? We all love a bit of positive\nreinforcement, don't we? So, instead of\nsaying, \"Don't use markdown in your\nresponse,\" You actually run the risk\nthere of it using markdown because it's\nlike, \"Ooh, markdown's in there.\" It\ndoesn't see sort of sentences as words\nas you use it. Like, if you say, \"Don't\nthink of a black cat,\" you immediately\nthink of a black cat, and that's kind of\nsimilar with large language models. So,\nhere, you should just say what you do\nactually want. So, your response here,\nI'll use the laser again. We love the\nlaser, don't we? Your response should be\ncomposed of smoothly flowing prose\nparagraphs. Ooh, that's even a nice\nsentence in its own, isn't it? And then,\nalso a big thing is that if you are\nfinding that it's still giving you\nformats that you don't really want, you\ncan actually match the prompt style to\nthe desired output. So, if you find\nClaude's actually having sort of like\noutput issues, you can steer it another\nway by inputting the sort of stuff you\ndo want. So, here, match your prompt\nstyle to the desired output. The\nformatting style used in your prompt may\ninfluence Claude's response style. So,\nif you're still experience steerability\nissues, like it keeps on going this way,\nbut you want it to go another way, then\nyou can try matching your prompt style\nto the desired output. So very\nimportant, and it's just a really nice\nway to make sure that you are steering\nit in the right way. It's super easy,\nand it can really improve your results.\nAnd there's more.\nYes.\nLike this one.\nOh, actually, [laughter] no. No, we're\nnot going on yet. We're actually just\ntalking about use prose. So, here, you\ncan see you can use this prompt, um,\n\"Write in clear flowing prose using\ncomplete paragraphs and sentences. Do\nnot do this. Instead of listing items,\nincorporate them naturally into\nsentences, and never output a series of\noverly short bullet points.\" So, use\nthis sort of academic stuff. Um, make\nsure you sort of save this prompt. I'll\nput them all in the description, or I\ncould put them in a note. I don't know.\nThey'll be somewhere. So, check out the\ndescription because that's where I'll\nput all of these, uh, prompts that I've\ngot here, and you can just copy and\npaste them, or put them into a project\nfile, which I'll show you how to do in a\nminute, to make sure that it always\nanswers in this way, which is great.\nOkay, next one.\nOne thing in academia is quite often we\nwant it to write a very specific way.\nNow, do not just say write in this way,\nthis way, this way, this way. It's\nactually way better to give it examples,\nbut not just any examples. This is what\nI mean. So, give it five examples\ninstead of describing. So, here from the\nofficial prompting docs, you can see\nexamples are one of the most reliable\nways to steer Claude's output and format\ntone and structure. But, here's the\nthing is that let's say you're writing\nan abstract or you're writing um a draft\nto a peer-reviewed uh paper, you need to\nmake sure that you provide it with a\nwide, diverse range of examples because\nif you give it examples that are too\nsimilar, it may start looking at\nstructures and formats that are maybe\nthere, maybe not, but if it's too\nsimilar, it'll be like, \"Oh, it's always\nlike this.\" And we don't want that. We\nwant to give it a wide range of examples\nto actually understand what we want from\nit. So, here we need examples that are\nrelevant and actually mirror your actual\nuse case closely. One thing I love doing\nis in a project file giving it examples\nof the things I actually want. So, I can\nsay, \"Here's five to 10 abstracts from\npapers that I want to submit in, you\nknow, use those.\" It could also be that\nyou have a document that has examples in\nit. Put that one up. It's very easy to\nsteer Claude to better academic output.\nAnd here we can see it needs to be\ndiverse, cover edge cases, and vary\nenough so that Claude doesn't pick up in\nunintended patterns. These large\nlanguage models are very good at finding\npatterns that to us aren't as obvious.\nAnd so, this is the sort of responding\nto reviewers um examples that we can\ngive. So, here we can see that we've got\na manuscript. So, this is all in code,\nbut if we're doing this in a prompting\nway, you know, it's very similar where\nwe say, \"Here's all of the reviewer\ncomments. Here's example one. Here's one\nthat's accept accepting a fair\ncriticism. Here's an example of a\nreviewer um sort of like response that\nis declining with justification. And\nhere is one which is correcting a\nmisreading from the reviewer.\" It's very\nimportant to have a range of um\nexamples. And you can see here, we've\ngot \"Draft a response to each numbered\ncomment.\" And then we can put it in. And\nyou can put something like this into the\nproject file. And here you can see,\n\"Include the awkward examples because if\nyou have similar examples, this will\nteach Claude a rule you did not intend\nand cannot see yourself.\" Because like I\nsaid, they love making connections,\nthese silly large language models. They\nare artificial intelligence after all,\nnot real intelligence. And then here,\n\"If you leave out the politely declining\nexample, Claude will agree to every\nsingle reviewer request.\" So, you're\nteaching Claude that, \"Oh, yeah, you\nalways must uh be positive in your\nresponses.\" Which is not what we want\nwhen we're looking at sort of like the\ndraft of a reviewer comments uh\ndocument. So, here you can see which is\nbad advice to hand a researcher,\nobviously. And look, don't get me wrong.\nIs I'm showing you this because then you\nneed to go away and have a look at the\noutput. One of my favorite ways to use\nlarge language models at the moment,\nactually, is to use it as a place to\ndisagree. And that's so important.\nPeople forget this is you can disagree\nwith large language models. Like it's so\neasy to do that. You get an output and\nyou say, \"Oh, I wouldn't do it that\nway.\" And then you can just use that as\na way of refining the output. Disagree\nwith it as much as you want and don't\nuse the output directly. You know, I\nthink it's like one of those things. You\nknow where\nyou have a really tough decision to make\nand they say, \"Oh, just flip a coin.\"\nBut the flipping the coin isn't to\nchoose, it's to tell you how you feel\nabout the decision. So, if you flip it,\nit lands on heads and you go, \"Oh, I\ndon't really didn't really want that.\"\nThen it tells you wanted the other\noption, you can choose the other option.\nThis is how I see these sort of outputs\nwith Claude for academia. You can\nactually just agree with it or disagree\nwith it and go, \"You know what? Now\nactually I prefer it this way.\" But,\nit's a great way to steer that\nconversation. Anyway, I'm getting ahead\nof of myself and away from the main\ntopic. So, here next we have to prompt\nit like a researcher. Very deep down\ninside in these prompting were things\nactually for research, and this is what\nI mean. So, here Claude's latest models\ncan find and synthesize information from\nmultiple sources, but for optimal\nresults we need to do certain things.\nSo, we need to provide clear success\ncriteria. Just as if you are teaching\nsomeone something, you need to say at\nthe end of this a successful outcome\nlooks like blah blah blah. And so,\ndefine what constitutes a successful\nanswer to your research question, and\nthen engage source verification. Ask\nClaude to verify information across\nmultiple sources, and for complex\nresearch tasks, this is like what we\nwant to do, use a structured approach.\nSo, in agentic AI we can say, \"Do this\nfirst, then this, then this, then this.\"\nThat structured approach agentic AI\nloves. It knows what to go through, and\nhere is the sort of literature review\nprompt that you can use. So, develop\nseveral competing hypotheses as you\ngather evidence. Track your confidence\nin each and say what would change it.\nSelf-critique your approach before\nconcluding. Before you finish, verify\nyour answer against the source\ndocuments. You can see there's kind of a\nstepwise approach here, which is exactly\nwhat we want when we are using Claude\nfor research purposes. And here, think\nthoroughly beats a step-by-step plan.\nSo, specify the destination and the\nstandard of evidence, and do not specify\nevery turn if you don't want to. So, the\ncounterintuitive bit is just asking it\nto think thoroughly. You don't have to\ngo into super detail with each\nindividual step. So, those are the five\nrules for making Claude work to your\nadvantage if you're using it for\nacademia and research. Let me know in\nthe comments what you would add.","duration_seconds":709,"is_auto_generated":true,"transcript_source":"youtube_transcript_api","transcript_status":"available","asr_quality_weight":0.6,"transcript_segments":[{"text":"30% is the amount of quality you can","start":0.0,"duration":4.92},{"text":"lose if you are prompting the wrong way","start":2.64,"duration":4.24},{"text":"in Claude. So, in this video, I'm going","start":4.92,"duration":4.08},{"text":"to share with you the actual real","start":6.88,"duration":4.68},{"text":"prompting guide from Anthropic","start":9.0,"duration":5.16},{"text":"themselves, and this is how you optimize","start":11.56,"duration":5.16},{"text":"for academia and research. Now, I've","start":14.16,"duration":4.72},{"text":"taken all of the information from the","start":16.72,"duration":4.76},{"text":"prompting best practices and actually","start":18.88,"duration":4.56},{"text":"had a look at them from the lens of","start":21.48,"duration":3.84},{"text":"academia and research, and these are the","start":23.44,"duration":4.24},{"text":"things that you can do right now to","start":25.32,"duration":4.68},{"text":"improve all of the","start":27.68,"duration":4.32},{"text":"responses you get. A lot of times people","start":30.0,"duration":3.36},{"text":"come to me and they say like, \"Oh, it's","start":32.0,"duration":3.0},{"text":"not very good.\" And that's because they","start":33.36,"duration":4.2},{"text":"are prompting the wrong way in a lot of","start":35.0,"duration":4.48},{"text":"ways. So, here are the five things you","start":37.56,"duration":4.04},{"text":"can do. The first one is if you have a","start":39.48,"duration":4.76},{"text":"document, make sure it's at the top and","start":41.6,"duration":4.24},{"text":"not the bottom. A lot of the times we","start":44.24,"duration":3.68},{"text":"go, \"Do this, do this, do this.\" And","start":45.84,"duration":4.24},{"text":"then give the document underneath. Now,","start":47.92,"duration":5.6},{"text":"I know you can actually add documents um","start":50.08,"duration":5.12},{"text":"separately as files now, but if you're","start":53.52,"duration":3.88},{"text":"pasting and copying text, this is what","start":55.2,"duration":4.44},{"text":"you should know is that the official","start":57.4,"duration":4.32},{"text":"prompt thing docs say that you should","start":59.64,"duration":3.6},{"text":"put Here, I'll put a laser on. There we","start":61.72,"duration":3.08},{"text":"are, that's better, isn't it? So, you","start":63.24,"duration":3.72},{"text":"should put long-form data at the top.","start":64.8,"duration":4.0},{"text":"So, place your long document near the","start":66.96,"duration":3.96},{"text":"top of your prompt above your query","start":68.8,"duration":3.92},{"text":"instructions and any examples. This","start":70.92,"duration":3.92},{"text":"improves performance across all of the","start":72.72,"duration":4.68},{"text":"Anthropic models, and surprisingly, it","start":74.84,"duration":4.52},{"text":"will probably do the same thing or","start":77.4,"duration":4.04},{"text":"unsurprisingly, it will probably do the","start":79.36,"duration":4.24},{"text":"same thing across other um large","start":81.44,"duration":4.36},{"text":"language models as well like Chat GPT,","start":83.6,"duration":4.08},{"text":"Gemini, and those sort of things. So,","start":85.8,"duration":4.56},{"text":"queries at the end can improve response","start":87.68,"duration":5.16},{"text":"quality by 30% in tests. And this is","start":90.36,"duration":4.36},{"text":"important because in academia, we are","start":92.84,"duration":4.08},{"text":"dealing with documents a lot. So, this","start":94.72,"duration":4.48},{"text":"is what a lot of us want to do is put","start":96.92,"duration":4.04},{"text":"the question and then copy and paste the","start":99.2,"duration":4.24},{"text":"paper underneath. But instead, what you","start":100.96,"duration":4.52},{"text":"need to do is put the paper or any","start":103.44,"duration":4.04},{"text":"context that you want, and then your","start":105.48,"duration":4.44},{"text":"question at the end. Now, this is very","start":107.48,"duration":5.56},{"text":"important if your text is over 20,000","start":109.92,"duration":4.68},{"text":"tokens. What does that look like? Well,","start":113.04,"duration":3.68},{"text":"you can use a calculator. I actually","start":114.6,"duration":4.88},{"text":"like using this one from Open AI, and I","start":116.72,"duration":4.68},{"text":"put in one of these papers, which is","start":119.48,"duration":3.96},{"text":"just like a typical length paper in my","start":121.4,"duration":4.2},{"text":"field, and you can see that a typical","start":123.44,"duration":5.4},{"text":"paper is approximately 10,000 tokens.","start":125.6,"duration":6.16},{"text":"So, if your papers are longer, you could","start":128.84,"duration":6.2},{"text":"easily run into the 20,000 token limit","start":131.76,"duration":5.76},{"text":"for this sort of prompting practice. So,","start":135.04,"duration":4.919},{"text":"I highly recommend that if you are","start":137.52,"duration":4.68},{"text":"putting in more than one paper, that you","start":139.959,"duration":5.0},{"text":"always put them first because otherwise,","start":142.2,"duration":4.8},{"text":"all of the details will get lost because","start":144.959,"duration":4.241},{"text":"of the token lengths and limits. That is","start":147.0,"duration":4.12},{"text":"an important thing. So, the next thing","start":149.2,"duration":3.96},{"text":"you should know about is if you are","start":151.12,"duration":4.4},{"text":"asking for information from a document,","start":153.16,"duration":4.08},{"text":"you need to ask it for quotes first.","start":155.52,"duration":4.12},{"text":"This is so important in academia and","start":157.24,"duration":4.92},{"text":"research because we all the time want to","start":159.64,"duration":4.6},{"text":"know the actual information, and this","start":162.16,"duration":4.56},{"text":"reduces hallucination. So, this is what","start":164.24,"duration":4.92},{"text":"the official prompting docs say, \"Ground","start":166.72,"duration":4.48},{"text":"responses in quotes.\" And this is what","start":169.16,"duration":4.44},{"text":"you can ask it for. And then, for long","start":171.2,"duration":4.44},{"text":"document task, which is what we're doing","start":173.6,"duration":4.96},{"text":"a lot in academia, \"Ask Claude to quote","start":175.64,"duration":5.12},{"text":"relevant parts of the document first","start":178.56,"duration":4.32},{"text":"before carrying out its its task.\" This","start":180.76,"duration":4.68},{"text":"helps sort of like anchor Claude and","start":182.88,"duration":5.4},{"text":"other large language models in the facts","start":185.44,"duration":4.24},{"text":"that it finds rather than trying to","start":188.28,"duration":3.84},{"text":"hallucinate them later. So, for example,","start":189.68,"duration":4.72},{"text":"this is the anti-hallucination prompt","start":192.12,"duration":4.32},{"text":"that you can use, \"Before answering,","start":194.4,"duration":4.04},{"text":"extract verbatim quotes from the","start":196.44,"duration":4.12},{"text":"documents that bear on the question,","start":198.44,"duration":4.16},{"text":"each tagged with its original source.\"","start":200.56,"duration":4.12},{"text":"That's so important. And then, answer","start":202.6,"duration":3.88},{"text":"using only these quotes. So, if you are","start":204.68,"duration":4.16},{"text":"looking for information from a document,","start":206.48,"duration":4.44},{"text":"make sure you do this because it will","start":208.84,"duration":4.44},{"text":"reduce your hallucination rate. That is","start":210.92,"duration":3.92},{"text":"so very important. All right then, these","start":213.28,"duration":4.0},{"text":"next ones are just as important. So, a","start":214.84,"duration":4.32},{"text":"lot of times when we're asking Claude to","start":217.28,"duration":4.16},{"text":"do something, we often say, \"Don't do a","start":219.16,"duration":4.0},{"text":"thing.\" But it actually responds far","start":221.44,"duration":4.28},{"text":"better to positive reinforcement. Don't","start":223.16,"duration":4.32},{"text":"we all? We all love a bit of positive","start":225.72,"duration":3.92},{"text":"reinforcement, don't we? So, instead of","start":227.48,"duration":4.36},{"text":"saying, \"Don't use markdown in your","start":229.64,"duration":4.12},{"text":"response,\" You actually run the risk","start":231.84,"duration":4.2},{"text":"there of it using markdown because it's","start":233.76,"duration":3.64},{"text":"like, \"Ooh, markdown's in there.\" It","start":236.04,"duration":3.8},{"text":"doesn't see sort of sentences as words","start":237.4,"duration":4.56},{"text":"as you use it. Like, if you say, \"Don't","start":239.84,"duration":4.24},{"text":"think of a black cat,\" you immediately","start":241.96,"duration":3.96},{"text":"think of a black cat, and that's kind of","start":244.08,"duration":3.879},{"text":"similar with large language models. So,","start":245.92,"duration":4.08},{"text":"here, you should just say what you do","start":247.959,"duration":4.0},{"text":"actually want. So, your response here,","start":250.0,"duration":3.28},{"text":"I'll use the laser again. We love the","start":251.959,"duration":3.361},{"text":"laser, don't we? Your response should be","start":253.28,"duration":4.239},{"text":"composed of smoothly flowing prose","start":255.32,"duration":4.199},{"text":"paragraphs. Ooh, that's even a nice","start":257.519,"duration":4.241},{"text":"sentence in its own, isn't it? And then,","start":259.519,"duration":4.721},{"text":"also a big thing is that if you are","start":261.76,"duration":4.52},{"text":"finding that it's still giving you","start":264.24,"duration":4.36},{"text":"formats that you don't really want, you","start":266.28,"duration":4.8},{"text":"can actually match the prompt style to","start":268.6,"duration":4.4},{"text":"the desired output. So, if you find","start":271.08,"duration":3.64},{"text":"Claude's actually having sort of like","start":273.0,"duration":4.52},{"text":"output issues, you can steer it another","start":274.72,"duration":5.4},{"text":"way by inputting the sort of stuff you","start":277.52,"duration":4.72},{"text":"do want. So, here, match your prompt","start":280.12,"duration":3.72},{"text":"style to the desired output. The","start":282.24,"duration":3.44},{"text":"formatting style used in your prompt may","start":283.84,"duration":3.84},{"text":"influence Claude's response style. So,","start":285.68,"duration":3.88},{"text":"if you're still experience steerability","start":287.68,"duration":3.92},{"text":"issues, like it keeps on going this way,","start":289.56,"duration":3.84},{"text":"but you want it to go another way, then","start":291.6,"duration":3.44},{"text":"you can try matching your prompt style","start":293.4,"duration":3.68},{"text":"to the desired output. So very","start":295.04,"duration":4.0},{"text":"important, and it's just a really nice","start":297.08,"duration":3.839},{"text":"way to make sure that you are steering","start":299.04,"duration":3.8},{"text":"it in the right way. It's super easy,","start":300.919,"duration":4.641},{"text":"and it can really improve your results.","start":302.84,"duration":4.52},{"text":"And there's more.","start":305.56,"duration":2.88},{"text":"Yes.","start":307.36,"duration":2.4},{"text":"Like this one.","start":308.44,"duration":2.88},{"text":"Oh, actually, [laughter] no. No, we're","start":309.76,"duration":3.4},{"text":"not going on yet. We're actually just","start":311.32,"duration":3.92},{"text":"talking about use prose. So, here, you","start":313.16,"duration":4.32},{"text":"can see you can use this prompt, um,","start":315.24,"duration":4.44},{"text":"\"Write in clear flowing prose using","start":317.48,"duration":4.32},{"text":"complete paragraphs and sentences. Do","start":319.68,"duration":4.44},{"text":"not do this. Instead of listing items,","start":321.8,"duration":3.72},{"text":"incorporate them naturally into","start":324.12,"duration":3.72},{"text":"sentences, and never output a series of","start":325.52,"duration":4.72},{"text":"overly short bullet points.\" So, use","start":327.84,"duration":4.88},{"text":"this sort of academic stuff. Um, make","start":330.24,"duration":4.08},{"text":"sure you sort of save this prompt. I'll","start":332.72,"duration":3.32},{"text":"put them all in the description, or I","start":334.32,"duration":2.88},{"text":"could put them in a note. I don't know.","start":336.04,"duration":2.72},{"text":"They'll be somewhere. So, check out the","start":337.2,"duration":2.8},{"text":"description because that's where I'll","start":338.76,"duration":3.04},{"text":"put all of these, uh, prompts that I've","start":340.0,"duration":3.48},{"text":"got here, and you can just copy and","start":341.8,"duration":4.28},{"text":"paste them, or put them into a project","start":343.48,"duration":3.8},{"text":"file, which I'll show you how to do in a","start":346.08,"duration":2.88},{"text":"minute, to make sure that it always","start":347.28,"duration":3.32},{"text":"answers in this way, which is great.","start":348.96,"duration":4.88},{"text":"Okay, next one.","start":350.6,"duration":5.6},{"text":"One thing in academia is quite often we","start":353.84,"duration":4.84},{"text":"want it to write a very specific way.","start":356.2,"duration":5.56},{"text":"Now, do not just say write in this way,","start":358.68,"duration":4.32},{"text":"this way, this way, this way. It's","start":361.76,"duration":3.48},{"text":"actually way better to give it examples,","start":363.0,"duration":4.12},{"text":"but not just any examples. This is what","start":365.24,"duration":4.16},{"text":"I mean. So, give it five examples","start":367.12,"duration":4.6},{"text":"instead of describing. So, here from the","start":369.4,"duration":4.12},{"text":"official prompting docs, you can see","start":371.72,"duration":3.96},{"text":"examples are one of the most reliable","start":373.52,"duration":4.8},{"text":"ways to steer Claude's output and format","start":375.68,"duration":4.36},{"text":"tone and structure. But, here's the","start":378.32,"duration":3.04},{"text":"thing is that let's say you're writing","start":380.04,"duration":4.68},{"text":"an abstract or you're writing um a draft","start":381.36,"duration":6.48},{"text":"to a peer-reviewed uh paper, you need to","start":384.72,"duration":5.4},{"text":"make sure that you provide it with a","start":387.84,"duration":5.92},{"text":"wide, diverse range of examples because","start":390.12,"duration":5.48},{"text":"if you give it examples that are too","start":393.76,"duration":4.72},{"text":"similar, it may start looking at","start":395.6,"duration":6.08},{"text":"structures and formats that are maybe","start":398.48,"duration":4.8},{"text":"there, maybe not, but if it's too","start":401.68,"duration":3.2},{"text":"similar, it'll be like, \"Oh, it's always","start":403.28,"duration":2.96},{"text":"like this.\" And we don't want that. We","start":404.88,"duration":3.96},{"text":"want to give it a wide range of examples","start":406.24,"duration":5.679},{"text":"to actually understand what we want from","start":408.84,"duration":5.8},{"text":"it. So, here we need examples that are","start":411.919,"duration":4.641},{"text":"relevant and actually mirror your actual","start":414.64,"duration":4.36},{"text":"use case closely. One thing I love doing","start":416.56,"duration":5.32},{"text":"is in a project file giving it examples","start":419.0,"duration":5.08},{"text":"of the things I actually want. So, I can","start":421.88,"duration":5.16},{"text":"say, \"Here's five to 10 abstracts from","start":424.08,"duration":5.16},{"text":"papers that I want to submit in, you","start":427.04,"duration":4.8},{"text":"know, use those.\" It could also be that","start":429.24,"duration":5.52}],"view_count_at_fetch":10931,"transcript_available":true,"source_universe_backfill":{"version":"source_universe_backfill.v1","backfilled_at":"2026-08-29T06:07:46.873948+00:00","resolver_warnings":[],"resolved_source_family":"video_media"},"transcript_last_attempted_at":"2026-08-27T06:19:36.537816+00:00"},"tags_json":[],"is_discovered_source":false},"transcript":{"segment_count":200,"markdown":"# Transcript\n\n## Segment 1\n\n**Speaker:** Unknown speaker\n\n30% is the amount of quality you can\n\n## Segment 2\n\n**Speaker:** Unknown speaker\n\nlose if you are prompting the wrong way\n\n## Segment 3\n\n**Speaker:** Unknown speaker\n\nin Claude. So, in this video, I'm going\n\n## Segment 4\n\n**Speaker:** Unknown speaker\n\nto share with you the actual real\n\n## Segment 5\n\n**Speaker:** Unknown speaker\n\nprompting guide from Anthropic\n\n## Segment 6\n\n**Speaker:** Unknown speaker\n\nthemselves, and this is how you optimize\n\n## Segment 7\n\n**Speaker:** Unknown speaker\n\nfor academia and research. Now, I've\n\n## Segment 8\n\n**Speaker:** Unknown speaker\n\ntaken all of the information from the\n\n## Segment 9\n\n**Speaker:** Unknown speaker\n\nprompting best practices and actually\n\n## Segment 10\n\n**Speaker:** Unknown speaker\n\nhad a look at them from the lens of\n\n## Segment 11\n\n**Speaker:** Unknown speaker\n\nacademia and research, and these are the\n\n## Segment 12\n\n**Speaker:** Unknown speaker\n\nthings that you can do right now to\n\n## Segment 13\n\n**Speaker:** Unknown speaker\n\nimprove all of the\n\n## Segment 14\n\n**Speaker:** Unknown speaker\n\nresponses you get. A lot of times people\n\n## Segment 15\n\n**Speaker:** Unknown speaker\n\ncome to me and they say like, \"Oh, it's\n\n## Segment 16\n\n**Speaker:** Unknown speaker\n\nnot very good.\" And that's because they\n\n## Segment 17\n\n**Speaker:** Unknown speaker\n\nare prompting the wrong way in a lot of\n\n## Segment 18\n\n**Speaker:** Unknown speaker\n\nways. So, here are the five things you\n\n## Segment 19\n\n**Speaker:** Unknown speaker\n\ncan do. The first one is if you have a\n\n## Segment 20\n\n**Speaker:** Unknown speaker\n\ndocument, make sure it's at the top and\n\n## Segment 21\n\n**Speaker:** Unknown speaker\n\nnot the bottom. A lot of the times we\n\n## Segment 22\n\n**Speaker:** Unknown speaker\n\ngo, \"Do this, do this, do this.\" And\n\n## Segment 23\n\n**Speaker:** Unknown speaker\n\nthen give the document underneath. Now,\n\n## Segment 24\n\n**Speaker:** Unknown speaker\n\nI know you can actually add documents um\n\n## Segment 25\n\n**Speaker:** Unknown speaker\n\nseparately as files now, but if you're\n\n## Segment 26\n\n**Speaker:** Unknown speaker\n\npasting and copying text, this is what\n\n## Segment 27\n\n**Speaker:** Unknown speaker\n\nyou should know is that the official\n\n## Segment 28\n\n**Speaker:** Unknown speaker\n\nprompt thing docs say that you should\n\n## Segment 29\n\n**Speaker:** Unknown speaker\n\nput Here, I'll put a laser on. There we\n\n## Segment 30\n\n**Speaker:** Unknown speaker\n\nare, that's better, isn't it? So, you\n\n## Segment 31\n\n**Speaker:** Unknown speaker\n\nshould put long-form data at the top.\n\n## Segment 32\n\n**Speaker:** Unknown speaker\n\nSo, place your long document near the\n\n## Segment 33\n\n**Speaker:** Unknown speaker\n\ntop of your prompt above your query\n\n## Segment 34\n\n**Speaker:** Unknown speaker\n\ninstructions and any examples. This\n\n## Segment 35\n\n**Speaker:** Unknown speaker\n\nimproves performance across all of the\n\n## Segment 36\n\n**Speaker:** Unknown speaker\n\nAnthropic models, and surprisingly, it\n\n## Segment 37\n\n**Speaker:** Unknown speaker\n\nwill probably do the same thing or\n\n## Segment 38\n\n**Speaker:** Unknown speaker\n\nunsurprisingly, it will probably do the\n\n## Segment 39\n\n**Speaker:** Unknown speaker\n\nsame thing across other um large\n\n## Segment 40\n\n**Speaker:** Unknown speaker\n\nlanguage models as well like Chat GPT,\n\n## Segment 41\n\n**Speaker:** Unknown speaker\n\nGemini, and those sort of things. So,\n\n## Segment 42\n\n**Speaker:** Unknown speaker\n\nqueries at the end can improve response\n\n## Segment 43\n\n**Speaker:** Unknown speaker\n\nquality by 30% in tests. And this is\n\n## Segment 44\n\n**Speaker:** Unknown speaker\n\nimportant because in academia, we are\n\n## Segment 45\n\n**Speaker:** Unknown speaker\n\ndealing with documents a lot. So, this\n\n## Segment 46\n\n**Speaker:** Unknown speaker\n\nis what a lot of us want to do is put\n\n## Segment 47\n\n**Speaker:** Unknown speaker\n\nthe question and then copy and paste the\n\n## Segment 48\n\n**Speaker:** Unknown speaker\n\npaper underneath. But instead, what you\n\n## Segment 49\n\n**Speaker:** Unknown speaker\n\nneed to do is put the paper or any\n\n## Segment 50\n\n**Speaker:** Unknown speaker\n\ncontext that you want, and then your\n\n## Segment 51\n\n**Speaker:** Unknown speaker\n\nquestion at the end. Now, this is very\n\n## Segment 52\n\n**Speaker:** Unknown speaker\n\nimportant if your text is over 20,000\n\n## Segment 53\n\n**Speaker:** Unknown speaker\n\ntokens. What does that look like? Well,\n\n## Segment 54\n\n**Speaker:** Unknown speaker\n\nyou can use a calculator. I actually\n\n## Segment 55\n\n**Speaker:** Unknown speaker\n\nlike using this one from Open AI, and I\n\n## Segment 56\n\n**Speaker:** Unknown speaker\n\nput in one of these papers, which is\n\n## Segment 57\n\n**Speaker:** Unknown speaker\n\njust like a typical length paper in my\n\n## Segment 58\n\n**Speaker:** Unknown speaker\n\nfield, and you can see that a typical\n\n## Segment 59\n\n**Speaker:** Unknown speaker\n\npaper is approximately 10,000 tokens.\n\n## Segment 60\n\n**Speaker:** Unknown speaker\n\nSo, if your papers are longer, you could\n\n## Segment 61\n\n**Speaker:** Unknown speaker\n\neasily run into the 20,000 token limit\n\n## Segment 62\n\n**Speaker:** Unknown speaker\n\nfor this sort of prompting practice. So,\n\n## Segment 63\n\n**Speaker:** Unknown speaker\n\nI highly recommend that if you are\n\n## Segment 64\n\n**Speaker:** Unknown speaker\n\nputting in more than one paper, that you\n\n## Segment 65\n\n**Speaker:** Unknown speaker\n\nalways put them first because otherwise,\n\n## Segment 66\n\n**Speaker:** Unknown speaker\n\nall of the details will get lost because\n\n## Segment 67\n\n**Speaker:** Unknown speaker\n\nof the token lengths and limits. That is\n\n## Segment 68\n\n**Speaker:** Unknown speaker\n\nan important thing. So, the next thing\n\n## Segment 69\n\n**Speaker:** Unknown speaker\n\nyou should know about is if you are\n\n## Segment 70\n\n**Speaker:** Unknown speaker\n\nasking for information from a document,\n\n## Segment 71\n\n**Speaker:** Unknown speaker\n\nyou need to ask it for quotes first.\n\n## Segment 72\n\n**Speaker:** Unknown speaker\n\nThis is so important in academia and\n\n## Segment 73\n\n**Speaker:** Unknown speaker\n\nresearch because we all the time want to\n\n## Segment 74\n\n**Speaker:** Unknown speaker\n\nknow the actual information, and this\n\n## Segment 75\n\n**Speaker:** Unknown speaker\n\nreduces hallucination. So, this is what\n\n## Segment 76\n\n**Speaker:** Unknown speaker\n\nthe official prompting docs say, \"Ground\n\n## Segment 77\n\n**Speaker:** Unknown speaker\n\nresponses in quotes.\" And this is what\n\n## Segment 78\n\n**Speaker:** Unknown speaker\n\nyou can ask it for. And then, for long\n\n## Segment 79\n\n**Speaker:** Unknown speaker\n\ndocument task, which is what we're doing\n\n## Segment 80\n\n**Speaker:** Unknown speaker\n\na lot in academia, \"Ask Claude to quote\n\n## Segment 81\n\n**Speaker:** Unknown speaker\n\nrelevant parts of the document first\n\n## Segment 82\n\n**Speaker:** Unknown speaker\n\nbefore carrying out its its task.\" This\n\n## Segment 83\n\n**Speaker:** Unknown speaker\n\nhelps sort of like anchor Claude and\n\n## Segment 84\n\n**Speaker:** Unknown speaker\n\nother large language models in the facts\n\n## Segment 85\n\n**Speaker:** Unknown speaker\n\nthat it finds rather than trying to\n\n## Segment 86\n\n**Speaker:** Unknown speaker\n\nhallucinate them later. So, for example,\n\n## Segment 87\n\n**Speaker:** Unknown speaker\n\nthis is the anti-hallucination prompt\n\n## Segment 88\n\n**Speaker:** Unknown speaker\n\nthat you can use, \"Before answering,\n\n## Segment 89\n\n**Speaker:** Unknown speaker\n\nextract verbatim quotes from the\n\n## Segment 90\n\n**Speaker:** Unknown speaker\n\ndocuments that bear on the question,\n\n## Segment 91\n\n**Speaker:** Unknown speaker\n\neach tagged with its original source.\"\n\n## Segment 92\n\n**Speaker:** Unknown speaker\n\nThat's so important. And then, answer\n\n## Segment 93\n\n**Speaker:** Unknown speaker\n\nusing only these quotes. So, if you are\n\n## Segment 94\n\n**Speaker:** Unknown speaker\n\nlooking for information from a document,\n\n## Segment 95\n\n**Speaker:** Unknown speaker\n\nmake sure you do this because it will\n\n## Segment 96\n\n**Speaker:** Unknown speaker\n\nreduce your hallucination rate. That is\n\n## Segment 97\n\n**Speaker:** Unknown speaker\n\nso very important. All right then, these\n\n## Segment 98\n\n**Speaker:** Unknown speaker\n\nnext ones are just as important. So, a\n\n## Segment 99\n\n**Speaker:** Unknown speaker\n\nlot of times when we're asking Claude to\n\n## Segment 100\n\n**Speaker:** Unknown speaker\n\ndo something, we often say, \"Don't do a\n\n## Segment 101\n\n**Speaker:** Unknown speaker\n\nthing.\" But it actually responds far\n\n## Segment 102\n\n**Speaker:** Unknown speaker\n\nbetter to positive reinforcement. Don't\n\n## Segment 103\n\n**Speaker:** Unknown speaker\n\nwe all? We all love a bit of positive\n\n## Segment 104\n\n**Speaker:** Unknown speaker\n\nreinforcement, don't we? So, instead of\n\n## Segment 105\n\n**Speaker:** Unknown speaker\n\nsaying, \"Don't use markdown in your\n\n## Segment 106\n\n**Speaker:** Unknown speaker\n\nresponse,\" You actually run the risk\n\n## Segment 107\n\n**Speaker:** Unknown speaker\n\nthere of it using markdown because it's\n\n## Segment 108\n\n**Speaker:** Unknown speaker\n\nlike, \"Ooh, markdown's in there.\" It\n\n## Segment 109\n\n**Speaker:** Unknown speaker\n\ndoesn't see sort of sentences as words\n\n## Segment 110\n\n**Speaker:** Unknown speaker\n\nas you use it. Like, if you say, \"Don't\n\n## Segment 111\n\n**Speaker:** Unknown speaker\n\nthink of a black cat,\" you immediately\n\n## Segment 112\n\n**Speaker:** Unknown speaker\n\nthink of a black cat, and that's kind of\n\n## Segment 113\n\n**Speaker:** Unknown speaker\n\nsimilar with large language models. So,\n\n## Segment 114\n\n**Speaker:** Unknown speaker\n\nhere, you should just say what you do\n\n## Segment 115\n\n**Speaker:** Unknown speaker\n\nactually want. So, your response here,\n\n## Segment 116\n\n**Speaker:** Unknown speaker\n\nI'll use the laser again. We love the\n\n## Segment 117\n\n**Speaker:** Unknown speaker\n\nlaser, don't we? Your response should be\n\n## Segment 118\n\n**Speaker:** Unknown speaker\n\ncomposed of smoothly flowing prose\n\n## Segment 119\n\n**Speaker:** Unknown speaker\n\nparagraphs. Ooh, that's even a nice\n\n## Segment 120\n\n**Speaker:** Unknown speaker\n\nsentence in its own, isn't it? And then,\n\n## Segment 121\n\n**Speaker:** Unknown speaker\n\nalso a big thing is that if you are\n\n## Segment 122\n\n**Speaker:** Unknown speaker\n\nfinding that it's still giving you\n\n## Segment 123\n\n**Speaker:** Unknown speaker\n\nformats that you don't really want, you\n\n## Segment 124\n\n**Speaker:** Unknown speaker\n\ncan actually match the prompt style to\n\n## Segment 125\n\n**Speaker:** Unknown speaker\n\nthe desired output. So, if you find\n\n## Segment 126\n\n**Speaker:** Unknown speaker\n\nClaude's actually having sort of like\n\n## Segment 127\n\n**Speaker:** Unknown speaker\n\noutput issues, you can steer it another\n\n## Segment 128\n\n**Speaker:** Unknown speaker\n\nway by inputting the sort of stuff you\n\n## Segment 129\n\n**Speaker:** Unknown speaker\n\ndo want. So, here, match your prompt\n\n## Segment 130\n\n**Speaker:** Unknown speaker\n\nstyle to the desired output. The\n\n## Segment 131\n\n**Speaker:** Unknown speaker\n\nformatting style used in your prompt may\n\n## Segment 132\n\n**Speaker:** Unknown speaker\n\ninfluence Claude's response style. So,\n\n## Segment 133\n\n**Speaker:** Unknown speaker\n\nif you're still experience steerability\n\n## Segment 134\n\n**Speaker:** Unknown speaker\n\nissues, like it keeps on going this way,\n\n## Segment 135\n\n**Speaker:** Unknown speaker\n\nbut you want it to go another way, then\n\n## Segment 136\n\n**Speaker:** Unknown speaker\n\nyou can try matching your prompt style\n\n## Segment 137\n\n**Speaker:** Unknown speaker\n\nto the desired output. So very\n\n## Segment 138\n\n**Speaker:** Unknown speaker\n\nimportant, and it's just a really nice\n\n## Segment 139\n\n**Speaker:** Unknown speaker\n\nway to make sure that you are steering\n\n## Segment 140\n\n**Speaker:** Unknown speaker\n\nit in the right way. It's super easy,\n\n## Segment 141\n\n**Speaker:** Unknown speaker\n\nand it can really improve your results.\n\n## Segment 142\n\n**Speaker:** Unknown speaker\n\nAnd there's more.\n\n## Segment 143\n\n**Speaker:** Unknown speaker\n\nYes.\n\n## Segment 144\n\n**Speaker:** Unknown speaker\n\nLike this one.\n\n## Segment 145\n\n**Speaker:** Unknown speaker\n\nOh, actually, [laughter] no. No, we're\n\n## Segment 146\n\n**Speaker:** Unknown speaker\n\nnot going on yet. We're actually just\n\n## Segment 147\n\n**Speaker:** Unknown speaker\n\ntalking about use prose. So, here, you\n\n## Segment 148\n\n**Speaker:** Unknown speaker\n\ncan see you can use this prompt, um,\n\n## Segment 149\n\n**Speaker:** Unknown speaker\n\n\"Write in clear flowing prose using\n\n## Segment 150\n\n**Speaker:** Unknown speaker\n\ncomplete paragraphs and sentences. Do\n\n## Segment 151\n\n**Speaker:** Unknown speaker\n\nnot do this. Instead of listing items,\n\n## Segment 152\n\n**Speaker:** Unknown speaker\n\nincorporate them naturally into\n\n## Segment 153\n\n**Speaker:** Unknown speaker\n\nsentences, and never output a series of\n\n## Segment 154\n\n**Speaker:** Unknown speaker\n\noverly short bullet points.\" So, use\n\n## Segment 155\n\n**Speaker:** Unknown speaker\n\nthis sort of academic stuff. Um, make\n\n## Segment 156\n\n**Speaker:** Unknown speaker\n\nsure you sort of save this prompt. I'll\n\n## Segment 157\n\n**Speaker:** Unknown speaker\n\nput them all in the description, or I\n\n## Segment 158\n\n**Speaker:** Unknown speaker\n\ncould put them in a note. I don't know.\n\n## Segment 159\n\n**Speaker:** Unknown speaker\n\nThey'll be somewhere. So, check out the\n\n## Segment 160\n\n**Speaker:** Unknown speaker\n\ndescription because that's where I'll\n\n## Segment 161\n\n**Speaker:** Unknown speaker\n\nput all of these, uh, prompts that I've\n\n## Segment 162\n\n**Speaker:** Unknown speaker\n\ngot here, and you can just copy and\n\n## Segment 163\n\n**Speaker:** Unknown speaker\n\npaste them, or put them into a project\n\n## Segment 164\n\n**Speaker:** Unknown speaker\n\nfile, which I'll show you how to do in a\n\n## Segment 165\n\n**Speaker:** Unknown speaker\n\nminute, to make sure that it always\n\n## Segment 166\n\n**Speaker:** Unknown speaker\n\nanswers in this way, which is great.\n\n## Segment 167\n\n**Speaker:** Unknown speaker\n\nOkay, next one.\n\n## Segment 168\n\n**Speaker:** Unknown speaker\n\nOne thing in academia is quite often we\n\n## Segment 169\n\n**Speaker:** Unknown speaker\n\nwant it to write a very specific way.\n\n## Segment 170\n\n**Speaker:** Unknown speaker\n\nNow, do not just say write in this way,\n\n## Segment 171\n\n**Speaker:** Unknown speaker\n\nthis way, this way, this way. It's\n\n## Segment 172\n\n**Speaker:** Unknown speaker\n\nactually way better to give it examples,\n\n## Segment 173\n\n**Speaker:** Unknown speaker\n\nbut not just any examples. This is what\n\n## Segment 174\n\n**Speaker:** Unknown speaker\n\nI mean. So, give it five examples\n\n## Segment 175\n\n**Speaker:** Unknown speaker\n\ninstead of describing. So, here from the\n\n## Segment 176\n\n**Speaker:** Unknown speaker\n\nofficial prompting docs, you can see\n\n## Segment 177\n\n**Speaker:** Unknown speaker\n\nexamples are one of the most reliable\n\n## Segment 178\n\n**Speaker:** Unknown speaker\n\nways to steer Claude's output and format\n\n## Segment 179\n\n**Speaker:** Unknown speaker\n\ntone and structure. But, here's the\n\n## Segment 180\n\n**Speaker:** Unknown speaker\n\nthing is that let's say you're writing\n\n## Segment 181\n\n**Speaker:** Unknown speaker\n\nan abstract or you're writing um a draft\n\n## Segment 182\n\n**Speaker:** Unknown speaker\n\nto a peer-reviewed uh paper, you need to\n\n## Segment 183\n\n**Speaker:** Unknown speaker\n\nmake sure that you provide it with a\n\n## Segment 184\n\n**Speaker:** Unknown speaker\n\nwide, diverse range of examples because\n\n## Segment 185\n\n**Speaker:** Unknown speaker\n\nif you give it examples that are too\n\n## Segment 186\n\n**Speaker:** Unknown speaker\n\nsimilar, it may start looking at\n\n## Segment 187\n\n**Speaker:** Unknown speaker\n\nstructures and formats that are maybe\n\n## Segment 188\n\n**Speaker:** Unknown speaker\n\nthere, maybe not, but if it's too\n\n## Segment 189\n\n**Speaker:** Unknown speaker\n\nsimilar, it'll be like, \"Oh, it's always\n\n## Segment 190\n\n**Speaker:** Unknown speaker\n\nlike this.\" And we don't want that. We\n\n## Segment 191\n\n**Speaker:** Unknown speaker\n\nwant to give it a wide range of examples\n\n## Segment 192\n\n**Speaker:** Unknown speaker\n\nto actually understand what we want from\n\n## Segment 193\n\n**Speaker:** Unknown speaker\n\nit. So, here we need examples that are\n\n## Segment 194\n\n**Speaker:** Unknown speaker\n\nrelevant and actually mirror your actual\n\n## Segment 195\n\n**Speaker:** Unknown speaker\n\nuse case closely. One thing I love doing\n\n## Segment 196\n\n**Speaker:** Unknown speaker\n\nis in a project file giving it examples\n\n## Segment 197\n\n**Speaker:** Unknown speaker\n\nof the things I actually want. So, I can\n\n## Segment 198\n\n**Speaker:** Unknown speaker\n\nsay, \"Here's five to 10 abstracts from\n\n## Segment 199\n\n**Speaker:** Unknown speaker\n\npapers that I want to submit in, you\n\n## Segment 200\n\n**Speaker:** Unknown speaker\n\nknow, use those.\" It could also be that","text":"[segment 0] Unknown speaker: 30% is the amount of quality you can\n[segment 1] Unknown speaker: lose if you are prompting the wrong way\n[segment 2] Unknown speaker: in Claude. So, in this video, I'm going\n[segment 3] Unknown speaker: to share with you the actual real\n[segment 4] Unknown speaker: prompting guide from Anthropic\n[segment 5] Unknown speaker: themselves, and this is how you optimize\n[segment 6] Unknown speaker: for academia and research. Now, I've\n[segment 7] Unknown speaker: taken all of the information from the\n[segment 8] Unknown speaker: prompting best practices and actually\n[segment 9] Unknown speaker: had a look at them from the lens of\n[segment 10] Unknown speaker: academia and research, and these are the\n[segment 11] Unknown speaker: things that you can do right now to\n[segment 12] Unknown speaker: improve all of the\n[segment 13] Unknown speaker: responses you get. A lot of times people\n[segment 14] Unknown speaker: come to me and they say like, \"Oh, it's\n[segment 15] Unknown speaker: not very good.\" And that's because they\n[segment 16] Unknown speaker: are prompting the wrong way in a lot of\n[segment 17] Unknown speaker: ways. So, here are the five things you\n[segment 18] Unknown speaker: can do. The first one is if you have a\n[segment 19] Unknown speaker: document, make sure it's at the top and\n[segment 20] Unknown speaker: not the bottom. A lot of the times we\n[segment 21] Unknown speaker: go, \"Do this, do this, do this.\" And\n[segment 22] Unknown speaker: then give the document underneath. Now,\n[segment 23] Unknown speaker: I know you can actually add documents um\n[segment 24] Unknown speaker: separately as files now, but if you're\n[segment 25] Unknown speaker: pasting and copying text, this is what\n[segment 26] Unknown speaker: you should know is that the official\n[segment 27] Unknown speaker: prompt thing docs say that you should\n[segment 28] Unknown speaker: put Here, I'll put a laser on. There we\n[segment 29] Unknown speaker: are, that's better, isn't it? So, you\n[segment 30] Unknown speaker: should put long-form data at the top.\n[segment 31] Unknown speaker: So, place your long document near the\n[segment 32] Unknown speaker: top of your prompt above your query\n[segment 33] Unknown speaker: instructions and any examples. This\n[segment 34] Unknown speaker: improves performance across all of the\n[segment 35] Unknown speaker: Anthropic models, and surprisingly, it\n[segment 36] Unknown speaker: will probably do the same thing or\n[segment 37] Unknown speaker: unsurprisingly, it will probably do the\n[segment 38] Unknown speaker: same thing across other um large\n[segment 39] Unknown speaker: language models as well like Chat GPT,\n[segment 40] Unknown speaker: Gemini, and those sort of things. So,\n[segment 41] Unknown speaker: queries at the end can improve response\n[segment 42] Unknown speaker: quality by 30% in tests. And this is\n[segment 43] Unknown speaker: important because in academia, we are\n[segment 44] Unknown speaker: dealing with documents a lot. So, this\n[segment 45] Unknown speaker: is what a lot of us want to do is put\n[segment 46] Unknown speaker: the question and then copy and paste the\n[segment 47] Unknown speaker: paper underneath. But instead, what you\n[segment 48] Unknown speaker: need to do is put the paper or any\n[segment 49] Unknown speaker: context that you want, and then your\n[segment 50] Unknown speaker: question at the end. Now, this is very\n[segment 51] Unknown speaker: important if your text is over 20,000\n[segment 52] Unknown speaker: tokens. What does that look like? Well,\n[segment 53] Unknown speaker: you can use a calculator. I actually\n[segment 54] Unknown speaker: like using this one from Open AI, and I\n[segment 55] Unknown speaker: put in one of these papers, which is\n[segment 56] Unknown speaker: just like a typical length paper in my\n[segment 57] Unknown speaker: field, and you can see that a typical\n[segment 58] Unknown speaker: paper is approximately 10,000 tokens.\n[segment 59] Unknown speaker: So, if your papers are longer, you could\n[segment 60] Unknown speaker: easily run into the 20,000 token limit\n[segment 61] Unknown speaker: for this sort of prompting practice. So,\n[segment 62] Unknown speaker: I highly recommend that if you are\n[segment 63] Unknown speaker: putting in more than one paper, that you\n[segment 64] Unknown speaker: always put them first because otherwise,\n[segment 65] Unknown speaker: all of the details will get lost because\n[segment 66] Unknown speaker: of the token lengths and limits. That is\n[segment 67] Unknown speaker: an important thing. So, the next thing\n[segment 68] Unknown speaker: you should know about is if you are\n[segment 69] Unknown speaker: asking for information from a document,\n[segment 70] Unknown speaker: you need to ask it for quotes first.\n[segment 71] Unknown speaker: This is so important in academia and\n[segment 72] Unknown speaker: research because we all the time want to\n[segment 73] Unknown speaker: know the actual information, and this\n[segment 74] Unknown speaker: reduces hallucination. So, this is what\n[segment 75] Unknown speaker: the official prompting docs say, \"Ground\n[segment 76] Unknown speaker: responses in quotes.\" And this is what\n[segment 77] Unknown speaker: you can ask it for. And then, for long\n[segment 78] Unknown speaker: document task, which is what we're doing\n[segment 79] Unknown speaker: a lot in academia, \"Ask Claude to quote\n[segment 80] Unknown speaker: relevant parts of the document first\n[segment 81] Unknown speaker: before carrying out its its task.\" This\n[segment 82] Unknown speaker: helps sort of like anchor Claude and\n[segment 83] Unknown speaker: other large language models in the facts\n[segment 84] Unknown speaker: that it finds rather than trying to\n[segment 85] Unknown speaker: hallucinate them later. So, for example,\n[segment 86] Unknown speaker: this is the anti-hallucination prompt\n[segment 87] Unknown speaker: that you can use, \"Before answering,\n[segment 88] Unknown speaker: extract verbatim quotes from the\n[segment 89] Unknown speaker: documents that bear on the question,\n[segment 90] Unknown speaker: each tagged with its original source.\"\n[segment 91] Unknown speaker: That's so important. And then, answer\n[segment 92] Unknown speaker: using only these quotes. So, if you are\n[segment 93] Unknown speaker: looking for information from a document,\n[segment 94] Unknown speaker: make sure you do this because it will\n[segment 95] Unknown speaker: reduce your hallucination rate. That is\n[segment 96] Unknown speaker: so very important. All right then, these\n[segment 97] Unknown speaker: next ones are just as important. So, a\n[segment 98] Unknown speaker: lot of times when we're asking Claude to\n[segment 99] Unknown speaker: do something, we often say, \"Don't do a\n[segment 100] Unknown speaker: thing.\" But it actually responds far\n[segment 101] Unknown speaker: better to positive reinforcement. Don't\n[segment 102] Unknown speaker: we all? We all love a bit of positive\n[segment 103] Unknown speaker: reinforcement, don't we? So, instead of\n[segment 104] Unknown speaker: saying, \"Don't use markdown in your\n[segment 105] Unknown speaker: response,\" You actually run the risk\n[segment 106] Unknown speaker: there of it using markdown because it's\n[segment 107] Unknown speaker: like, \"Ooh, markdown's in there.\" It\n[segment 108] Unknown speaker: doesn't see sort of sentences as words\n[segment 109] Unknown speaker: as you use it. Like, if you say, \"Don't\n[segment 110] Unknown speaker: think of a black cat,\" you immediately\n[segment 111] Unknown speaker: think of a black cat, and that's kind of\n[segment 112] Unknown speaker: similar with large language models. So,\n[segment 113] Unknown speaker: here, you should just say what you do\n[segment 114] Unknown speaker: actually want. So, your response here,\n[segment 115] Unknown speaker: I'll use the laser again. We love the\n[segment 116] Unknown speaker: laser, don't we? Your response should be\n[segment 117] Unknown speaker: composed of smoothly flowing prose\n[segment 118] Unknown speaker: paragraphs. Ooh, that's even a nice\n[segment 119] Unknown speaker: sentence in its own, isn't it? And then,\n[segment 120] Unknown speaker: also a big thing is that if you are\n[segment 121] Unknown speaker: finding that it's still giving you\n[segment 122] Unknown speaker: formats that you don't really want, you\n[segment 123] Unknown speaker: can actually match the prompt style to\n[segment 124] Unknown speaker: the desired output. So, if you find\n[segment 125] Unknown speaker: Claude's actually having sort of like\n[segment 126] Unknown speaker: output issues, you can steer it another\n[segment 127] Unknown speaker: way by inputting the sort of stuff you\n[segment 128] Unknown speaker: do want. So, here, match your prompt\n[segment 129] Unknown speaker: style to the desired output. The\n[segment 130] Unknown speaker: formatting style used in your prompt may\n[segment 131] Unknown speaker: influence Claude's response style. So,\n[segment 132] Unknown speaker: if you're still experience steerability\n[segment 133] Unknown speaker: issues, like it keeps on going this way,\n[segment 134] Unknown speaker: but you want it to go another way, then\n[segment 135] Unknown speaker: you can try matching your prompt style\n[segment 136] Unknown speaker: to the desired output. So very\n[segment 137] Unknown speaker: important, and it's just a really nice\n[segment 138] Unknown speaker: way to make sure that you are steering\n[segment 139] Unknown speaker: it in the right way. It's super easy,\n[segment 140] Unknown speaker: and it can really improve your results.\n[segment 141] Unknown speaker: And there's more.\n[segment 142] Unknown speaker: Yes.\n[segment 143] Unknown speaker: Like this one.\n[segment 144] Unknown speaker: Oh, actually, [laughter] no. No, we're\n[segment 145] Unknown speaker: not going on yet. We're actually just\n[segment 146] Unknown speaker: talking about use prose. So, here, you\n[segment 147] Unknown speaker: can see you can use this prompt, um,\n[segment 148] Unknown speaker: \"Write in clear flowing prose using\n[segment 149] Unknown speaker: complete paragraphs and sentences. Do\n[segment 150] Unknown speaker: not do this. Instead of listing items,\n[segment 151] Unknown speaker: incorporate them naturally into\n[segment 152] Unknown speaker: sentences, and never output a series of\n[segment 153] Unknown speaker: overly short bullet points.\" So, use\n[segment 154] Unknown speaker: this sort of academic stuff. Um, make\n[segment 155] Unknown speaker: sure you sort of save this prompt. I'll\n[segment 156] Unknown speaker: put them all in the description, or I\n[segment 157] Unknown speaker: could put them in a note. I don't know.\n[segment 158] Unknown speaker: They'll be somewhere. So, check out the\n[segment 159] Unknown speaker: description because that's where I'll\n[segment 160] Unknown speaker: put all of these, uh, prompts that I've\n[segment 161] Unknown speaker: got here, and you can just copy and\n[segment 162] Unknown speaker: paste them, or put them into a project\n[segment 163] Unknown speaker: file, which I'll show you how to do in a\n[segment 164] Unknown speaker: minute, to make sure that it always\n[segment 165] Unknown speaker: answers in this way, which is great.\n[segment 166] Unknown speaker: Okay, next one.\n[segment 167] Unknown speaker: One thing in academia is quite often we\n[segment 168] Unknown speaker: want it to write a very specific way.\n[segment 169] Unknown speaker: Now, do not just say write in this way,\n[segment 170] Unknown speaker: this way, this way, this way. It's\n[segment 171] Unknown speaker: actually way better to give it examples,\n[segment 172] Unknown speaker: but not just any examples. This is what\n[segment 173] Unknown speaker: I mean. So, give it five examples\n[segment 174] Unknown speaker: instead of describing. So, here from the\n[segment 175] Unknown speaker: official prompting docs, you can see\n[segment 176] Unknown speaker: examples are one of the most reliable\n[segment 177] Unknown speaker: ways to steer Claude's output and format\n[segment 178] Unknown speaker: tone and structure. But, here's the\n[segment 179] Unknown speaker: thing is that let's say you're writing\n[segment 180] Unknown speaker: an abstract or you're writing um a draft\n[segment 181] Unknown speaker: to a peer-reviewed uh paper, you need to\n[segment 182] Unknown speaker: make sure that you provide it with a\n[segment 183] Unknown speaker: wide, diverse range of examples because\n[segment 184] Unknown speaker: if you give it examples that are too\n[segment 185] Unknown speaker: similar, it may start looking at\n[segment 186] Unknown speaker: structures and formats that are maybe\n[segment 187] Unknown speaker: there, maybe not, but if it's too\n[segment 188] Unknown speaker: similar, it'll be like, \"Oh, it's always\n[segment 189] Unknown speaker: like this.\" And we don't want that. We\n[segment 190] Unknown speaker: want to give it a wide range of examples\n[segment 191] Unknown speaker: to actually understand what we want from\n[segment 192] Unknown speaker: it. So, here we need examples that are\n[segment 193] Unknown speaker: relevant and actually mirror your actual\n[segment 194] Unknown speaker: use case closely. One thing I love doing\n[segment 195] Unknown speaker: is in a project file giving it examples\n[segment 196] Unknown speaker: of the things I actually want. So, I can\n[segment 197] Unknown speaker: say, \"Here's five to 10 abstracts from\n[segment 198] Unknown speaker: papers that I want to submit in, you\n[segment 199] Unknown speaker: know, use those.\" It could also be that","segments":[{"id":"137a618d-07af-4f58-85bf-45a382135227","segment_index":0,"speaker_name":null,"start_seconds":null,"end_seconds":null,"text":"30% is the amount of quality you can"},{"id":"331a83b5-7b40-4676-a38d-9b82ca5191a5","segment_index":1,"speaker_name":null,"start_seconds":null,"end_seconds":null,"text":"lose if you are prompting the wrong way"},{"id":"ddfd8cd9-4e61-49d6-80dc-f8b07fb4969d","segment_index":2,"speaker_name":null,"start_seconds":null,"end_seconds":null,"text":"in Claude. So, in this video, I'm going"},{"id":"26b4861c-0742-4846-a37a-4a3f64cb9db0","segment_index":3,"speaker_name":null,"start_seconds":null,"end_seconds":null,"text":"to share with you the actual real"},{"id":"722afe74-dc1b-4666-9376-b1032afb6d62","segment_index":4,"speaker_name":null,"start_seconds":null,"end_seconds":null,"text":"prompting guide from Anthropic"},{"id":"bef9d05f-20c2-474b-88a9-8a396c72b2c8","segment_index":5,"speaker_name":null,"start_seconds":null,"end_seconds":null,"text":"themselves, and this is how you optimize"},{"id":"ae6813aa-cd6d-44d0-b1ec-e0b68be59db4","segment_index":6,"speaker_name":null,"start_seconds":null,"end_seconds":null,"text":"for academia and research. Now, I've"},{"id":"21968eb5-d813-4f7b-a8f0-a02f4466d505","segment_index":7,"speaker_name":null,"start_seconds":null,"end_seconds":null,"text":"taken all of the information from the"},{"id":"a623eb33-2045-4b46-942c-2dc539dc34a1","segment_index":8,"speaker_name":null,"start_seconds":null,"end_seconds":null,"text":"prompting best practices and actually"},{"id":"49c41667-167e-4c90-97b9-638d793bf149","segment_index":9,"speaker_name":null,"start_seconds":null,"end_seconds":null,"text":"had a look at them from the lens of"},{"id":"32a1c41e-c9ed-422d-b23d-e5fd0c706b67","segment_index":10,"speaker_name":null,"start_seconds":null,"end_seconds":null,"text":"academia and research, and these are the"},{"id":"19435603-8822-4559-9b10-9b006d66d114","segment_index":11,"speaker_name":null,"start_seconds":null,"end_seconds":null,"text":"things that you can do right now to"},{"id":"164318b6-cd1c-4ec1-af74-7dcf12ce619b","segment_index":12,"speaker_name":null,"start_seconds":null,"end_seconds":null,"text":"improve all of the"},{"id":"d771df60-5aaf-4cc9-b931-f5bf95d5f42a","segment_index":13,"speaker_name":null,"start_seconds":null,"end_seconds":null,"text":"responses you get. A lot of times people"},{"id":"174037c9-78ff-4bd8-9bdc-f91964976f83","segment_index":14,"speaker_name":null,"start_seconds":null,"end_seconds":null,"text":"come to me and they say like, \"Oh, it's"},{"id":"b3289fcc-4720-442b-b5b9-3f9844fa0e6d","segment_index":15,"speaker_name":null,"start_seconds":null,"end_seconds":null,"text":"not very good.\" And that's because they"},{"id":"132a8fd8-3d71-4eb4-a62c-e30752cd2a23","segment_index":16,"speaker_name":null,"start_seconds":null,"end_seconds":null,"text":"are prompting the wrong way in a lot of"},{"id":"6e73ad48-f8dd-4ca2-b73c-e2ed9edcb9ae","segment_index":17,"speaker_name":null,"start_seconds":null,"end_seconds":null,"text":"ways. So, here are the five things you"},{"id":"6ca85075-e2a8-48fc-997c-c9de050658a3","segment_index":18,"speaker_name":null,"start_seconds":null,"end_seconds":null,"text":"can do. The first one is if you have a"},{"id":"d1363c84-3d37-4683-8303-f7021a470d48","segment_index":19,"speaker_name":null,"start_seconds":null,"end_seconds":null,"text":"document, make sure it's at the top and"},{"id":"6a592245-b76c-48bb-ac96-11e41a7d1e8f","segment_index":20,"speaker_name":null,"start_seconds":null,"end_seconds":null,"text":"not the bottom. A lot of the times we"},{"id":"6c915673-6e4a-4e80-9a25-fc4f0a4ef8b9","segment_index":21,"speaker_name":null,"start_seconds":null,"end_seconds":null,"text":"go, \"Do this, do this, do this.\" And"},{"id":"dd89938f-274a-40f0-963c-b1946b5e77be","segment_index":22,"speaker_name":null,"start_seconds":null,"end_seconds":null,"text":"then give the document underneath. Now,"},{"id":"977a2c29-2007-49d1-8220-4db7101c3b4e","segment_index":23,"speaker_name":null,"start_seconds":null,"end_seconds":null,"text":"I know you can actually add documents um"},{"id":"d03fb166-ad27-4e59-87a1-a4705a64c185","segment_index":24,"speaker_name":null,"start_seconds":null,"end_seconds":null,"text":"separately as files now, but if you're"},{"id":"3276041d-0fcf-4b06-9679-8bdd080fa433","segment_index":25,"speaker_name":null,"start_seconds":null,"end_seconds":null,"text":"pasting and copying text, this is what"},{"id":"d6124159-04ee-4ba9-96ce-ef2bfc74569c","segment_index":26,"speaker_name":null,"start_seconds":null,"end_seconds":null,"text":"you should know is that the official"},{"id":"adaa84d0-a638-4ef9-9710-995526d98617","segment_index":27,"speaker_name":null,"start_seconds":null,"end_seconds":null,"text":"prompt thing docs say that you should"},{"id":"2ee57363-954c-4bd1-aa29-b726e0f6503d","segment_index":28,"speaker_name":null,"start_seconds":null,"end_seconds":null,"text":"put Here, I'll put a laser on. 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