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.
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.
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.
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.
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.
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.
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.
Prepare documents for RAG when you're feeding a corpus into a retrieval system to query later; use long document analysis when you're reading one oversized file in a single session and want a summary. This grounds and chunks for storage across many docs — it never summarizes.
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.
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.
A set of source documents chunked to your budgeted size on clean boundaries, each piece self-contained, with grounding rules attached so the model answers from the retrieved text. It's retrieval-ready preparation you load into your own retriever — not the retriever itself, which you still build and run.
Work the three steps in your own AI tool: budget the chunk size with the context-window estimator, ground each source with the long-input formatter, then split on clean boundaries with the long-prompt splitter. NewPrompt supplies the prompts and order; you run them and load the output into your retriever.
Usually the preparation failed, not the retriever. Chunks split mid-thought arrive stripped of context; chunks sized wrong crowd out the answer; ungrounded sources let the model invent. Re-run the workflow — budget the size, ground against drift, split on real boundaries — before you blame the retrieval layer.
Complete build journeys that include this workflow as a stage.
The full path to a support agent you can put in front of customers — write its instructions, ground it in your docs, route and handle tickets, then evaluate and cost-control it before it goes live.
The full path to an AI research assistant — define its scope, organize the source corpus, ground responses in references, extract key facts, synthesize findings, check groundedness, then validate it for use.
The full path to a retrieval system that returns grounded answers — understand the corpus, chunk and ground it, extract and classify the metadata, then evaluate that retrieval actually works.
The full path to a support operation, not just a bot — stand up the knowledge base, route the tickets, add the AI agent, integrate your stack, close the feedback loop, evaluate, and deploy.
The full path to knowledge that's findable by people and AI — plan the taxonomy, structure it for search, write the articles, tag the metadata, make it retrievable, then ship it maintainable.
Get AI to actually read a document that's too big for one prompt — fit it to the model, split it cleanly, package the parts, and analyze them without losing the thread.
Pull a single coherent view out of a stack of sources — package them together, summarize each faithfully, then have AI synthesize across them instead of one at a time.
Turn messy text into structured data you can trust enough to feed another system — bound the source, extract the fields, force clean JSON, and validate before it flows downstream.