@IntuitMachine: OpenAI self-leaked its Deep Re...
@IntuitMachine
42 views
Jun 27, 2025
1
OpenAI self-leaked its Deep Research prompts and it's a goldmine of ideas! Let's analyze this in detail!
2
Prompting patterns used
3
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.”
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.”
4
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). intuitionmachine.gumroad.com/l/agentic/zo5h…
5
BTW, Anthropic had published their own Deep Research approach earlier.
View Tweet
6
Subscribe to this feed ($2.99/month) to continue to receive banger AI ideas!