[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"article-deep-research-prompt-framework-ai-reports-en":3,"article-related-deep-research-prompt-framework-ai-reports-en":30,"series-tools-bc6e4805-34c1-456c-9628-2a1746db6c21":84},{"id":4,"slug":5,"title":6,"content":7,"summary":8,"source":9,"source_url":10,"author":11,"image_url":12,"cover_image":12,"category":13,"language":14,"translated_content":11,"related_article_id":15,"keywords":16,"key_takeaways":22,"views":26,"created_at":27,"published_at":28,"topic_cluster_id":29},"bc6e4805-34c1-456c-9628-2a1746db6c21","deep-research-prompt-framework-ai-reports-en","Deep Research Prompt Framework for Better AI Reports","\u003Cp data-speakable=\"summary\">FindSkill.ai shows how to turn vague questions into cited AI research prompts.\u003C\u002Fp>\u003Cp>\u003Ca href=\"https:\u002F\u002Ffindskill.ai\u002Fskills\u002Fproductivity\u002Fdeep-research-prompt-framework\u002F\" target=\"_blank\" rel=\"noopener\">FindSkill.ai\u003C\u002Fa> has published a detailed prompt framework for people who want better research from AI tools. The page was last updated on June 11, 2026, and it packages the advice into a reusable skill aimed at \u003Ca href=\"\u002Ftag\u002Fchatgpt\">ChatGPT\u003C\u002Fa>, \u003Ca href=\"https:\u002F\u002Fclaude.ai\" target=\"_blank\" rel=\"noopener\">Claude\u003C\u002Fa>, \u003Ca href=\"https:\u002F\u002Fgemini.google.com\" target=\"_blank\" rel=\"noopener\">Gemini\u003C\u002Fa>, and \u003Ca href=\"https:\u002F\u002Fwww.perplexity.ai\" target=\"_blank\" rel=\"noopener\">Perplexity\u003C\u002Fa>.\u003C\u002Fp>\u003Cp>The core idea is simple: if you want a deep answer, you need a deep prompt. FindSkill’s framework treats the prompt itself as the product, then breaks that product into sections for role, question framing, scope, depth, citations, output structure, and guardrails.\u003C\u002Fp>\u003Ctable>\u003Cthead>\u003Ctr>\u003Cth>Detail\u003C\u002Fth>\u003Cth>Value\u003C\u002Fth>\u003C\u002Ftr>\u003C\u002Fthead>\u003Ctbody>\u003Ctr>\u003Ctd>Skill level\u003C\u002Ftd>\u003Ctd>Intermediate\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Ctd>Estimated time\u003C\u002Ftd>\u003Ctd>10 min\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Ctd>User rating\u003C\u002Ftd>\u003Ctd>4.8 \u002F 5\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Ctd>Last updated\u003C\u002Ftd>\u003Ctd>June 11, 2026\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Ctd>Example topic\u003C\u002Ftd>\u003Ctd>Lab-grown meat commercialization\u003C\u002Ftd>\u003C\u002Ftr>\u003C\u002Ftbody>\u003C\u002Ftable>\u003Ch2>What FindSkill is really teaching\u003C\u002Fh2>\u003Cp>This is less about prompt tricks and more about research discipline. The guide argues that weak prompts create shallow summaries, while structured prompts push AI models toward sourced, multi-perspective analysis.\u003C\u002Fp>\n\u003Cfigure class=\"my-6\">\u003Cimg src=\"https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1781155090319-y5q2.png\" alt=\"Deep Research Prompt Framework for Better AI Reports\" class=\"rounded-xl w-full\" loading=\"lazy\" \u002F>\u003C\u002Ffigure>\n\u003Cp>That framing matters because the page is aimed at people using AI for actual decisions, not casual curiosity. Venture investors, policy teams, product managers, and analysts all need different outputs, and the framework tries to make those differences explicit before the model starts writing.\u003C\u002Fp>\u003Cp>FindSkill also makes a practical point that many AI users ignore: depth and breadth compete with each other. If you ask for everything, you usually get a mushy overview. If you narrow the scope, the model can go deeper and cite better evidence.\u003C\u002Fp>\u003Cul>\u003Cli>Structured prompts beat long, rambling prompts.\u003C\u002Fli>\u003Cli>Specific wording reduces ambiguity and improves output quality.\u003C\u002Fli>\u003Cli>Citations are treated as mandatory, not optional.\u003C\u002Fli>\u003Cli>Prompt design changes the shape of the final answer.\u003C\u002Fli>\u003C\u002Ful>\u003Ch2>The eight-part prompt anatomy\u003C\u002Fh2>\u003Cp>The strongest part of the guide is its breakdown of the prompt into eight components. That gives users a repeatable workflow instead of a vague “ask better questions” slogan.\u003C\u002Fp>\u003Cp>The first four pieces handle the research setup: persona, research question, scope boundaries, and depth. The second four handle delivery: citation requirements, output structure, quality guardrails, and revision instructions. That division is smart because it mirrors how serious analysts work.\u003C\u002Fp>\u003Cblockquote>“The quality of AI research output is directly proportional to the quality of the prompt that requests it.”\u003C\u002Fblockquote>\u003Cp>That line captures the whole thesis. The guide is built around the idea that \u003Ca href=\"\u002Ftag\u002Fprompt-engineering\">prompt engineering\u003C\u002Fa> is a form of research design, and that the design choices matter before the model ever opens a browser or starts drafting.\u003C\u002Fp>\u003Cp>One especially useful section is the persona template. Instead of saying “you are a research assistant,” the framework suggests role-specific identities such as a senior technology analyst, a strategy consultant, or a PhD researcher. The point is to set the model’s vocabulary and analytical angle early.\u003C\u002Fp>\u003Cp>The research-question section uses a PICO-style structure adapted for AI. It asks users to define the problem, the investigation, the comparison, and the outcome. That is a good move because it forces the user to stop writing fuzzy prompts like “tell me about climate change” and start writing prompts that can actually be answered.\u003C\u002Fp>\u003Cul>\u003Cli>\u003Cstrong>Persona\u003C\u002Fstrong> sets the expertise level and point of view.\u003C\u002Fli>\u003Cli>\u003Cstrong>Question framing\u003C\u002Fstrong> turns a topic into an answerable research task.\u003C\u002Fli>\u003Cli>\u003Cstrong>Scope\u003C\u002Fstrong> limits time, geography, sector, and source type.\u003C\u002Fli>\u003Cli>\u003Cstrong>Depth\u003C\u002Fstrong> defines word count, source count, and analysis style.\u003C\u002Fli>\u003Cli>\u003Cstrong>Citations\u003C\u002Fstrong> specify format and source rules.\u003C\u002Fli>\u003Cli>\u003Cstrong>Output structure\u003C\u002Fstrong> makes the result usable immediately.\u003C\u002Fli>\u003Cli>\u003Cstrong>Guardrails\u003C\u002Fstrong> reduce shortcuts and unsupported claims.\u003C\u002Fli>\u003Cli>\u003Cstrong>Revision rules\u003C\u002Fstrong> help users improve the prompt after the first draft.\u003C\u002Fli>\u003C\u002Ful>\u003Ch2>Why this matters for people using AI for research\u003C\u002Fh2>\u003Cp>Most AI users still treat research prompts like search queries. FindSkill’s framework pushes in the other direction: treat the prompt like a brief for an analyst. That shift changes the output more than a lot of people expect.\u003C\u002Fp>\n\u003Cfigure class=\"my-6\">\u003Cimg src=\"https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1781155090543-7nj5.png\" alt=\"Deep Research Prompt Framework for Better AI Reports\" class=\"rounded-xl w-full\" loading=\"lazy\" \u002F>\u003C\u002Ffigure>\n\u003Cp>For example, the guide’s lab-grown meat sample prompt asks for inline numbered citations, a full report format, price-parity comparisons, regulatory approvals, consumer acceptance data, and five-year market projections. That is a serious brief, and it tells the model exactly what kind of evidence to gather.\u003C\u002Fp>\u003Cp>There is also a clear practical benefit for teams. If different people in a company use the same prompt structure, they can compare outputs more easily and spot weak sourcing faster. That matters in product research, investment memos, and competitive analysis, where a vague claim can waste hours.\u003C\u002Fp>\u003Cp>The framework also reflects a broader truth about AI research tools like \u003Ca href=\"https:\u002F\u002Fopenai.com\u002Findex\u002Fchatgpt\u002F\" target=\"_blank\" rel=\"noopener\">ChatGPT\u003C\u002Fa>, \u003Ca href=\"https:\u002F\u002Fwww.anthropic.com\u002Fclaude\" target=\"_blank\" rel=\"noopener\">Claude\u003C\u002Fa>, and \u003Ca href=\"https:\u002F\u002Fwww.microsoft.com\u002Fen-us\u002Fmicrosoft-copilot\" target=\"_blank\" rel=\"noopener\">Microsoft Copilot\u003C\u002Fa>: the model can only optimize around the instructions it gets. If the instructions are sloppy, the output will usually be sloppy too.\u003C\u002Fp>\u003Cp>That is why the citation rules in the framework matter so much. The guide explicitly tells users to require numbered citations, distinguish primary from secondary sources, and mark unsourced claims as unverified. Those constraints make the prompt harder to write, but they also make the output easier to trust.\u003C\u002Fp>\u003Ch2>How it compares with simpler prompt advice\u003C\u002Fh2>\u003Cp>Plenty of prompt guides stop at “be specific” and “ask for sources.” FindSkill goes further by giving users a structure they can reuse across topics, teams, and AI tools.\u003C\u002Fp>\u003Cp>The difference shows up in the level of detail. A basic prompt might ask for a summary of a market. FindSkill’s framework asks for the audience, the time window, the source types, the word count, the comparison baseline, and the final report format. That extra scaffolding is what makes the output more useful.\u003C\u002Fp>\u003Cp>Here is the practical comparison:\u003C\u002Fp>\u003Cul>\u003Cli>A vague prompt asks for a topic; FindSkill asks for a framed research question.\u003C\u002Fli>\u003Cli>A basic prompt asks for “sources”; FindSkill requires citation format and source rules.\u003C\u002Fli>\u003Cli>A casual prompt leaves structure open; FindSkill prescribes report sections.\u003C\u002Fli>\u003Cli>A simple prompt often returns a summary; FindSkill aims for decision-ready analysis.\u003C\u002Fli>\u003C\u002Ful>\u003Cp>The guide is also opinionated about trade-offs, which I like. It does not pretend that one prompt can produce exhaustive depth and exhaustive breadth at the same time. Instead, it tells users to chain prompts when they need both.\u003C\u002Fp>\u003Cp>That advice is more useful than it sounds. In practice, a two-stage workflow often works better than a monster prompt: first define the scope and research question, then ask for the report. That keeps the model focused and usually reduces hallucinated filler.\u003C\u002Fp>\u003Ch2>The takeaway for developers and knowledge workers\u003C\u002Fh2>\u003Cp>FindSkill’s \u003Ca href=\"https:\u002F\u002Ffindskill.ai\u002Fskills\u002Fproductivity\u002Fdeep-research-prompt-framework\u002F\" target=\"_blank\" rel=\"noopener\">Deep Research Prompt Framework\u003C\u002Fa> is a clean example of how prompt engineering has matured from one-off tricks into reusable process design. It is aimed at people who need AI to produce evidence-backed work, not just polished prose.\u003C\u002Fp>\u003Cp>If you build internal tools, write prompts for clients, or use AI for research-heavy work, this framework is worth copying into your workflow. The biggest lesson is that good research output begins with a prompt that defines the job, the boundaries, and the evidence standard.\u003C\u002Fp>\u003Cp>My read is that the teams who adopt structured research prompts now will spend less time cleaning up weak AI drafts later. The next step is obvious: turn this framework into templates for the kinds of research your team repeats most often, then compare the results against your current prompts.\u003C\u002Fp>","FindSkill.ai breaks down a prompt framework for getting cited, multi-source research from ChatGPT, Claude, Gemini, and Perplexity.","findskill.ai","https:\u002F\u002Ffindskill.ai\u002Fskills\u002Fproductivity\u002Fdeep-research-prompt-framework\u002F",null,"https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1781155090319-y5q2.png","tools","en","e515b195-1a6e-4cc0-a2eb-452e918d8859",[17,18,19,20,21],"AI research prompts","prompt engineering","citation-backed research","FindSkill.ai","deep research",[23,24,25],"FindSkill.ai turns AI research into a structured prompt-writing process.","The framework centers on eight parts: persona, question, scope, depth, citations, structure, guardrails, and revision.","Its main value is better sourcing and more usable outputs for serious research work.",0,"2026-06-11T05:17:40.460525+00:00","2026-06-11T05:17:40.448+00:00","a7343b93-37cc-4634-a2bc-707f6275bdb6",{"tags":31,"relatedLang":43,"relatedPosts":47},[32,35,37,39,41],{"name":33,"slug":34},"Deep Research","deep-research",{"name":19,"slug":36},"citation-backed-research",{"name":18,"slug":38},"prompt-engineering",{"name":20,"slug":40},"findskillai",{"name":17,"slug":42},"ai-research-prompts",{"id":15,"slug":44,"title":45,"language":46},"deep-research-prompt-framework-ai-reports-zh","AI 研究報告要先寫好提示詞","zh",[48,54,60,66,72,78],{"id":49,"slug":50,"title":51,"cover_image":52,"image_url":52,"created_at":53,"category":13},"2d7d46e9-e387-449c-97d9-2e99eddb3496","6-part-prompt-scoring-turns-vague-prompts-into-usable-ones-en","6-part prompt scoring turns vague prompts into usable ones","https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1781151511274-pig7.png","2026-06-11T04:18:00.860733+00:00",{"id":55,"slug":56,"title":57,"cover_image":58,"image_url":58,"created_at":59,"category":13},"d8f6f103-5502-4fcc-862d-5271e1cc3d69","last30days-skill-best-reason-stop-trusting-search-alone-en","last30days-skill is the best reason to stop trusting search alone","https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1781122670367-luxf.png","2026-06-10T20:17:22.707907+00:00",{"id":61,"slug":62,"title":63,"cover_image":64,"image_url":64,"created_at":65,"category":13},"57a4012c-5884-47f1-babd-aa193a10468e","15-ai-coding-assistant-tools-2026-en","15 AI Coding Assistant Tools for 2026","https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1781114586004-3e6d.png","2026-06-10T18:02:27.929561+00:00",{"id":67,"slug":68,"title":69,"cover_image":70,"image_url":70,"created_at":71,"category":13},"908bd6d7-ba5a-40f0-8000-e785fd1372f5","cuda-oxide-rust-ptx-kernels-en","cuda-oxide turns Rust into PTX kernels","https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1781110153405-wqt4.png","2026-06-10T16:48:44.105254+00:00",{"id":73,"slug":74,"title":75,"cover_image":76,"image_url":76,"created_at":77,"category":13},"741f86d7-bc7c-4ff6-8bc8-fbc0e7d780bd","gpu-programming-core-software-skill-en","GPU programming is becoming a core software 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Tools","2026-03-26T01:27:43.127519+00:00",{"id":96,"slug":97,"title":98,"created_at":99},"165f9a19-c92d-46ba-b3f0-7125f662921d","rag-2026-transforming-enterprise-ai-en","How RAG in 2026 is Transforming Enterprise AI","2026-03-26T01:28:11.485236+00:00",{"id":101,"slug":102,"title":103,"created_at":104},"6a2a8e6e-b956-49d8-be12-cc47bdc132b2","mastering-ai-prompts-2026-guide-en","Mastering AI Prompts: A 2026 Guide for Developers","2026-03-26T01:29:07.835148+00:00",{"id":106,"slug":107,"title":108,"created_at":109},"3ab2c67e-4664-4c67-a013-687a2f605814","garry-tan-open-sources-claude-code-toolkit-en","Garry Tan Open-Sources a Claude Code Toolkit","2026-03-26T08:26:20.245934+00:00",{"id":111,"slug":112,"title":113,"created_at":114},"66a7cbf8-7e76-41d4-9bbf-eaca9761bf69","github-ai-projects-to-watch-in-2026-en","20 GitHub AI Projects to Watch in 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