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.
The moment a document is bigger than the model's context window, the usual moves fail. Paste it and it gets truncated. Cut it arbitrarily and you sever a sentence or a table mid-thought, and the analysis comes back confused. The fix isn't a bigger paste — it's knowing whether it fits, splitting on real boundaries when it doesn't, packaging the parts so the model treats them as one source, and only then analyzing. In that order, a 200-page export becomes something the AI can reason over.
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.
Check whether it even fits
Before splitting anything, measure the document against your model's window. If it fits with room left for a response, you can skip the chunking and go straight to analysis.
Split on real boundaries
If it overflows, break it where the document already breaks — sections, paragraphs, code fences — never mid-sentence. Clean cuts are what keep each chunk independently readable.
Package the parts as one source
Wrap the chunks in delimiters and labels so the model knows where the source begins and ends, and treats the pieces as one document rather than loose text to act on.
Analyze, then stitch the findings
Run a structured summary or analysis across the parts, then combine the per-chunk results into one coherent answer. Structure is what keeps a long output from drifting.
A document that wouldn't fit in one prompt is analyzed end to end — fitted, split on clean boundaries, packaged as one source, and summarized into a single coherent result instead of a truncated guess.
Use this workflow to analyze one oversized document in a single sitting, ending in a structured summary. Use the AI RAG Context Workflow when you're grounding and chunking many documents into a retrieval system for later querying — it stores for search and never summarizes. Same context tools, opposite goal.
You get one structured summary or analysis of the whole document — not a per-chunk pile. Step 4 runs a structured-summary prompt across the packaged parts, then stitches the per-chunk results into a single coherent answer, so a 200-page export reads as one analysis instead of a truncated guess.
Run the four steps in order: estimate the fit with the context-window estimator, split on real boundaries with the long-prompt splitter, package the parts as one delimited source with the long-input formatter, then analyze with the structured-summary prompt. You run each prompt in your own AI tool and own the review.
Usually the split severed meaning or a chunk got dropped — the analysis is only as good as the sections you feed and verify. Re-split on clean boundaries in step 2, confirm every part is delimited and labeled in step 3, then check the stitched summary against the source. You own that verification.
Then step 1 tells you so and you skip straight to analysis. The splitting and packaging steps only earn their place when the document actually overflows the window.
Yes — every tool runs in your browser and only produces prompts. You paste those and your content into your own AI. Nothing is uploaded.
Complete build journeys that include this workflow as a stage.
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.
A document too long for the model has to be split — but a blind split makes the AI forget earlier parts, drift on definitions, and lean on whatever it saw last. Here's how to split and synthesize without losing the thread.
You line up a transcript, three docs, and the project context and assume it'll all fit. But a model's window is shared with its own answer — so "it fits" can still truncate the reply. Here's how to estimate whether your input fits the context window, answer and margin included.
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.
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.
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.