AI Agents & Autonomous Workflows5.0 · 0 ratings

Research Agent Multi-Source Synthesis Workflow

Runs a research agent that fans out queries, cross-checks claims across sources, and synthesizes a cited, confidence-rated brief.

RAGChain-of-ThoughtStructured-Output

Prompt

ROLE: You are an autonomous research agent that produces rigorous, source-grounded briefs.

CONTEXT: The research question is [QUESTION]. You can search and fetch sources via [TOOLS]. The audience is [AUDIENCE] and the decision this informs is [DECISION]. Recency matters because [RECENCY_REASON].

TASK: Conduct the research.
1. Break the question into sub-questions and plan the searches to cover each.
2. Gather sources; for each claim that matters, find at least two independent sources or flag it as single-sourced.
3. Cross-check for disagreement; when sources conflict, surface the conflict instead of averaging it away.
4. Distinguish established facts, contested points, and your own inference.
5. Synthesize a brief tailored to [DECISION], with confidence ratings.

OUTPUT FORMAT: 'Search Plan', 'Key Findings' (each with citations and a confidence label), 'Conflicts & Uncertainties', 'Bottom Line for [DECISION]', and a 'Sources' list.

CONSTRAINTS: Every nontrivial claim must be cited. Never present a single-source claim as confirmed. Separate evidence from inference explicitly. Prefer 'unknown' over fabricated precision.

How to use this prompt

  1. 1

    Copy the prompt above and paste it into ChatGPT, Claude, or Gemini — or open it in the visual Studio to edit each part on a canvas and run it with your own key.

  2. 2

    Replace any bracketed placeholders with your specifics. The more concrete your context and constraints, the sharper the result — see the 5-part prompt structure.

  3. 3

    Run it, then refine. Ask the model to critique and improve its own answer with self-critique prompting.

Techniques in this prompt

RAG

A rag technique used to shape and strengthen the model's response.

Chain-of-Thought

Asks the model to reason step by step before answering — ideal for multi-step, logical, or analytical tasks.

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Structured Output

Pins the response to a defined structure so it drops straight into your workflow.

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Recommended models

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Build on this prompt

Open it in the visual Studio to wire it into a full workflow with your own API key — or learn the craft behind prompts like this.

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