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OpenAI self-leaked its Deep Research prompts and it's a goldmine of ideas! Let's analyze this in detail!
Prompting patterns used
1. System Message Prompt Prompting Patterns Used: a) Structured Response Pattern Description: A prompt that explicitly specifies format, expectations, and output style—ensuring clarity and replicability, as outlined in the knowledge source (“Structured Response Pattern” and “Grammatic Scaffolding”). Quoted Instance: “Your task is to analyze the health question the user poses.” “Focus on data-rich insights: include specific figures, trends, statistics, and measurable outcomes…” “Summarize data in a way that could be turned into charts or tables, and call this out in the response…” b) Constraint Signaling Pattern Description: Explicitly states constraints or requirements, reducing ambiguity (“Constraint Signaling Pattern”). Quoted Instance: “Prioritize reliable, up-to-date sources: peer-reviewed research, health organizations (e.g., WHO, CDC), regulatory agencies, or pharmaceutical earnings reports.” “Be analytical, avoid generalities, and ensure that each section supports data-backed reasoning…” c) Declarative Intent Pattern Description: Prompt spells out the intention and the reasoning approach—aligning model action with user needs. Quoted Instance: “Your task is to analyze the health question the user poses.” 2. System Message with MCP Prompt Prompting Patterns Used: a) Tool Use Governance Description: Directs the model to use a specific internal tool and sets priorities for information sources. This is part of the “Tool Use Governance” and “Input/Output Transformation Chaining” patterns. Quoted Instance: “Include an internal file lookup tool to retrieve information from our own internal data sources. If you’ve already retrieved a file, do not call fetch again for that same file. Prioritize inclusion of that data.” b) Compositional Flow Pattern Description: This pattern chains actions or retrieval steps (e.g., “use internal, then external sources”), echoing “Sequential Composition” or “Dynamic Task Orchestration.” Quoted Instance: “Prioritize inclusion of that data [from internal sources].” 3. Suggest Rewriting Prompt Prompting Patterns Used: a) Instructional Framing Voice Description: The prompt frames the model’s task as writing instructions for someone else, not performing the research itself. This is a hallmark of the “Instructional Framing Voice” pattern. Quoted Instance: “Your job is to produce a set of instructions for a researcher that will complete the task. Do NOT complete the task yourself, just provide instructions on how to complete it.” b) Constraint Signaling Pattern Description: Enumerates detailed requirements and constraints, ensuring instructions are complete and unambiguous. Quoted Instance: “Include all known user preferences and explicitly list key attributes or dimensions to consider.” “If certain attributes are essential for a meaningful output but the user has not provided them, explicitly state that they are open-ended…” c) Output Structure/Format Signaling Description: Specifies the expected output structure or format, closely linked to the “Structured Response Pattern.” Quoted Instance: “You should include the expected output format in the prompt.” “If you determine that including a table will help… you must explicitly request that the researcher provide them.” 4. Suggest Clarifying Prompt Prompting Patterns Used: a) Implicit Assumption Clarification Pattern Description: Prompt focuses on surfacing ambiguities and missing information—encouraging the model to seek clarity before acting (“Implicit Assumption Clarification Pattern”). Quoted Instance: “Ask clarifying questions that would help you or another researcher produce a more specific, efficient, and relevant answer.” “Identify essential attributes that were not specified in the user’s request…” b) Feedback Integration Pattern Description: Directs iterative, conversational clarification to refine scope and reduce ambiguity, echoing “Feedback Integration Pattern.” Quoted Instance: “If there are multiple open questions, list them clearly in bullet format for readability.” “Format for conversational use… Aim for a natural tone while still being precise.”
Use my agentic pattern playbook to analyze leaked prompts and improve then (includes a GPT where you can use 4.1, o3 to power the analysis). <a target="_blank" href="https://intuitionmachine.gumroad.com/l/agentic/zo5hrj3" color="blue">intuitionmachine.gumroad.com/l/agentic/zo5h…</a>
BTW, Anthropic had published their own Deep Research approach earlier. <a target="_blank" href="https://x.com/IntuitMachine/status/1933688096052404604" color="blue">x.com/IntuitMachine/…</a>
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