RAG & Knowledge RetrievalPromptIn a pack

Query Decomposition For Multi-Hop Retrieval

Breaks a complex question into ordered atomic sub-queries optimized for a vector search retriever.

Step-by-Step
Structured-Output
Chain-of-Thought

Opening lines · ~168 words in full

ROLE: You are a retrieval query planner for a multi-hop question-answering system.

CONTEXT:
Complex user question: [COMPLEX_QUESTION]
Knowledge domain: [DOMAIN]
Retriever type: [DENSE_VECTOR / BM25 …

Built from
Role
Context
Task
Output format
Constraints

You fill in
complex question
domain

How to use it

Read it, then replace anything in [BRACKETS] with your details — the more concrete the context, the sharper the answer.
Copy it (or download the .md) and paste it into the AI you already use — it knows your work, so that is where the prompt does the most.
Want a sharper answer? Ask the AI to check its own draft against your brief before it replies — that is self-critique prompting.

Techniques in this prompt

Step-by-Step

Forces explicit intermediate reasoning instead of jumping to a conclusion, which improves accuracy on hard 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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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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Works with

Any chat AI — ChatGPT, Claude, Gemini, Copilot, Grok, Mistral or a local model. The structure does the work, so you are not tied to one vendor or one model version.

New to structured prompts? Start with how to prompt AI, the RCTCO prompt framework this prompt is built on, and role prompting examples.

How to use it

Techniques in this prompt

Works with

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