AI RAG Context Workflow
Prepare documents for a RAG system so retrieved answers stay accurate — budget the chunk size to the model, ground the sources against drift, and split them on clean boundaries for retrieval.
The problem
RAG answers are only as good as the chunks behind them. Split documents arbitrarily and a retrieved fragment arrives stripped of the context that made it meaningful. Size chunks wrong for the model and you either waste the window or starve it. Skip grounding and the model fills gaps with confident invention. The retrieval layer gets the blame, but the failure usually happened earlier — in how the source was prepared. That preparation is unglamorous and decisive: budget the chunk size, ground the sources, and split where the meaning actually breaks.
Recommended workflow
Each step uses an existing NewPrompt tool, pre-filled by a matching resource. Open the resource to read it, or jump straight into the tool with the inputs ready.
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Budget the chunk size to the model
Decide how big a chunk can be once you account for the query, the retrieved neighbors, and room to answer. Chunk size is a budgeting decision before it's a splitting one.
Goal A target chunk size that fits retrieval plus a real response.
Open this step in Context Window Estimator -
Ground the sources against drift
Package each source with grounding rules so the model answers from the retrieved text — and says so when the text doesn't cover the question — instead of inventing.
Goal Sources framed so the model stays inside them.
Open this step in Long Input Formatter -
Split on clean boundaries for retrieval
Chunk to the budgeted size on real boundaries, so each retrieved piece is self-contained and still makes sense pulled out of order.
Goal Retrieval-ready chunks that hold meaning on their own.
Open this step in Long Prompt Splitter
Expected outcome
A set of source documents chunked to the right size, grounded against hallucination, and split so each retrieved piece stands on its own — the preparation that lets a RAG system return accurate, sourced answers instead of confident guesses.
Best for
- Preparing a knowledge base for retrieval
- Fixing a RAG system that returns vague or wrong answers
- Chunking documents so retrieval stays accurate
Not for
- Analyzing or summarizing a single document in one sitting — use the AI Long Document Analysis Workflow
- Content that already fits in the prompt with no retrieval layer
FAQ
How is this different from the AI Long Document Analysis Workflow?
Long document analysis reads one oversized document in a single session and ends with a summary. This prepares many documents for a retrieval system to query later — it grounds and chunks for storage and never summarizes. The tools overlap; the goal is the opposite.
Why budget chunk size before splitting?
Because the right chunk size depends on what else shares the window at query time — the question, the other retrieved chunks, and the response. Splitting first and sizing later is how you end up re-chunking everything.
Does grounding replace a good retriever?
No — it complements it. Even perfect retrieval fails if the model treats retrieved text as a suggestion. Grounding tells it to answer from the source and admit gaps, which is what keeps RAG answers honest.