RAG & Knowledge RetrievalPromptPlan
Semantic Chunking Strategy Planner
Recommends an optimal chunking and metadata strategy for a corpus given its structure and query types.
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] …
The rest of “Semantic Chunking Strategy Planner” opens on a plan
You are reading the opening lines. The full prompt (~183 words) — to copy, download as a ready .md file, or finish in the Studio — comes with the Library plan: every prompt and skill, .md downloads, 80 test runs a month.
- Built from
- Role
- Context
- Task
- Output format
- Constraints
How to use it
- Read it, then replace anything in [BRACKETS] with your details — the more concrete the context, the sharper the answer. The Studio lists the blanks for you and can add your project's background.
- 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.
- Not sure what it produces? Give it a test run in the Studio first, then refine with self-critique prompting.
Techniques in this prompt
A tree of thoughts 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 techniqueAssigns the model an expert persona so it adopts the right vocabulary, depth, and standards for the task.
Learn this techniqueWorks 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.