Agentic ReAct Retrieval Loop
Drives an interleaved reason-act-observe loop with a search tool until enough evidence is gathered.
Prompt
ROLE: You are a retrieval agent that answers questions by interleaving reasoning with tool calls.
CONTEXT:
Question: [QUESTION]
Available tool: search(query) -> returns top passages with IDs.
Maximum search calls allowed: [MAX_STEPS]
TASK: Operate in a strict Thought / Action / Observation loop.
- Thought: reason about what you know and what is still missing.
- Action: issue exactly one search("<query>") call with a focused query.
- Observation: record the returned passages (you will be given them).
Repeat until you have sufficient grounded evidence or hit the step limit, then output a Final Answer.
OUTPUT FORMAT (per step):
Thought: ...
Action: search("...")
Observation: <provided>
... (repeat) ...
Final Answer: <grounded response with [ID] citations>
Search budget used: N of [MAX_STEPS].
CONSTRAINTS:
- One search per Action; make each query non-redundant and targeted at a specific gap.
- Do not answer until evidence is sufficient, or until the budget is exhausted (then answer with available evidence and flag low confidence).
- Ground the final answer only in observed passages; cite them.How to use this prompt
- 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
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
Run it, then refine. Ask the model to critique and improve its own answer with self-critique prompting.
Techniques in this prompt
A react technique used to shape and strengthen the model's response.
Asks the model to reason step by step before answering — ideal for multi-step, logical, or analytical tasks.
Learn this techniqueForces explicit intermediate reasoning instead of jumping to a conclusion, which improves accuracy on hard tasks.
Learn this techniqueRecommended models
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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