Chunk Long Documents — Sections Survive the Split
Documentation mode chunks structured documents at heading boundaries, tells the model to reconstruct the section structure, and answers with original headings.
View Resource →Context Tools
Long Prompt Splitter
"Message too long"? Don't slice it blindly. Paste the content and get a chunked context package: boundary-aware splitting (sections, paragraphs, code blocks kept atomic — character cuts only as a counted last resort), every chunk wrapped in continuity markers with wait/acknowledge rules, and a final-chunk trigger for the real task. Runs entirely in your browser.
Paste the oversized content — document, transcript, codebase, or long prompt. It never leaves the browser.
Delivery Mode
Each mode changes the wait/acknowledge rules and the final-chunk trigger — Codebase also prefers file boundaries.
Split Preview (live — the plan, not the package)
Chunked Context Package
Message Too Long — the Fix That Doesn't Butcher Content
The "message too long" error has a structural fix: split at paragraph boundaries into sequenced chunks with wait rules, instead of pasting fragments and hoping.
View Resource →Prepare Multi-Message Analysis — the Task Arrives Last
In multi-part delivery, WHERE the task lands decides the answer quality. Analysis mode places the task behind the all-delivered announcement — never before.
View Resource →Preserve Context Across Chunks — the Continuity Contract
Chunks without continuity rules are read as separate documents. The continuity contract — numbering, wait rules, acknowledgements, completion — keeps N parts one document.
View Resource →Send Large Project Context — Code and Docs in One Package
Project context is mixed: code, notes, and docs together. Send it as one sequenced package the model holds complete before any work begins.
View Resource →Split a Book for AI Analysis — Whole-Work Reading in Parts
Book-length analysis needs the WHOLE work read before any judgment. Analysis mode delivers the chapters in order and triggers the full analysis only at the end.
View Resource →Split Large Codebase Context — File Boundaries, Atomic Fences
Code split mid-function is unreviewable. Codebase mode splits at file boundaries, keeps fenced blocks atomic, and lists the files in every chunk.
View Resource →Split Large Prompts — Boundary-Aware, Never Blind
Splitting a long prompt by character count butchers it. Boundary-aware chunking splits at sections and paragraphs, keeps code blocks atomic, and counts every forced cut.
View Resource →Split Research Material — Sources Stay Distinct
A research corpus is many sources, not one text. Split at source boundaries so each study stays distinct — and synthesis waits for the full corpus.
View Resource →Split Transcripts for AI — Meetings in Order, Decisions at the End
Hours of meetings exceed any message limit. Split transcripts at speaker-turn boundaries, deliver in order, and extract decisions only after every meeting has arrived.
View Resource →AI Long Document Analysis Workflow
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.
View Playbook →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.
View Playbook →How it works
Paste the content that won't fit in one message, optionally add the task the AI should perform once everything has arrived, and pick the target model, a chunk size (~4K, ~8K, or ~16K tokens per chunk — converted to characters with content-type-aware ratios), and a delivery mode. The engine splits at meaningful boundaries in strict preference order — section and file boundaries first, then paragraphs, then lines, with character cuts only as a counted last resort — and never breaks inside a fenced code block when it can avoid it. Click Split Into Chunks for the chunked context package: delivery instructions, a strategy summary with boundary-quality stats, every chunk wrapped in "Chunk i of N" continuity markers with wait-and-acknowledge rules, and the final chunk carrying the all-delivered announcement plus your task. A feasibility check warns when even split chunks can't share one conversation window. Per-chunk copy buttons make the send-one-message-at-a-time workflow painless. Nothing leaves your browser.
Use cases
- Fixing "message too long" without butchering the content
- Feeding a multi-file codebase into one conversation
- Sending book-length material for whole-work analysis
- Keeping the AI waiting until every part has arrived
Pro tips
- Use the per-chunk copy buttons and actually wait for the acknowledgement between sends — the "Received chunk i of N" handshake is what catches a lost or skipped chunk before it silently corrupts the analysis.
- Put the real task in the Task field instead of improvising it after the last paste: the engine places it behind the all-chunks-delivered announcement, which is exactly when the model should first see it.
- Pick Codebase mode for source code even if it feels like "just text" — file-boundary preference and atomic code fences are the difference between reviewable chunks and code soup.
- Watch the Fits Window stat: chunking solves the per-message limit, not the total window. If it says NO, the package's warning lists the real options — bigger window, separate conversations, or a handoff package.
FAQ
Why not just split every 5,000 characters?
Because blind cuts destroy meaning: a paragraph severed mid-sentence, a code block split through a function, a table separated from its header. This engine parses the content into segments — headings, paragraphs, fenced code blocks, file markers — and packs chunks along those boundaries in preference order. Character cuts exist only as a last resort, and the strategy section counts them so you can see exactly how clean the split was.
What are the continuity markers for?
They're the difference between sending parts and the model understanding parts. Every chunk announces "Chunk i of N", instructs the model to acknowledge and wait, and the final chunk explicitly declares delivery complete before triggering the task. Without these rules, models routinely answer after the first chunk, treat each part as a complete document, or lose track of how the parts relate.
Does splitting solve the context window limit?
Only the per-message limit — and the tool is honest about the difference. All chunks still land in the same conversation, so the model's window must hold the total. The feasibility check does that math (total estimate plus chunk overhead plus response room against the model's window) and warns when one conversation can't hold it, with the real options: larger-window model, separate conversations, or a compact handoff instead of full content.
What changes between the delivery modes?
The rules the chunks carry. Standard simply marks the parts. AI Reading Mode forbids responding until the final part announces completion — the strictest waiting contract. Analysis Mode triggers a full analysis from the final chunk with chunk-level citations. Documentation Mode prefers section boundaries and tells the model to reconstruct the heading structure. Codebase Mode prefers file boundaries, keeps fences atomic, lists the files in each chunk, and forbids reviewing before the codebase is complete.
Is the split lossless?
Yes, by construction: chunk bodies are pure slices of your input, and concatenating them reproduces the original text exactly — the markers live outside the content. The single exception is a hard character cut (a single line longer than the entire chunk budget), which has to insert a break; the strategy section reports every hard cut, and in normal content there are zero.
How is this different from the Context Window Estimator?
Different verbs, in sequence. The Estimator MEASURES — will it fit, how much budget is there — and when the answer is no, it points here. The Splitter FITS — it actually produces the sendable chunks. They share the same model table and token-estimation logic, so the numbers agree between the two tools.
Should I split a long conversation history with this?
Usually not — that's the neighboring tool's job. If you're trying to CONTINUE work in a new chat, you don't want the full transcript in pieces; you want a compact state package (decisions, constraints, current task), which is the Context Handoff Builder. Split when the AI genuinely needs all the original material; hand off when it needs what the material established.