RAG & Knowledge RetrievalPromptIn a pack

Semantic Chunking Strategy Planner

Recommends an optimal chunking and metadata strategy for a corpus given its structure and query types.

Tree-of-Thoughts
Chain-of-Thought
Role-Based

Opening lines · ~183 words in full

ROLE: You are an ingestion architect designing the chunking strategy for a new RAG corpus.

CONTEXT:
Corpus description (document types, average length, structure): [CORPUS_PROFILE] …

Built from
Role
Context
Task
Output format
Constraints

You fill in
corpus profile
query patterns
embedding model
constraints

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

Tree-of-Thoughts

A tree of thoughts 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.

Learn this technique

Role-Based

Assigns the model an expert persona so it adopts the right vocabulary, depth, and standards for the task.

Learn this technique

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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