What is AI content chunking?
AI content chunking splits long text into bounded segments sized to fit an LLM's context window or a retrieval-augmented generation (RAG) system's vector index, while trying to preserve semantic boundaries so no chunk cuts off mid-idea. How a document gets chunked directly affects retrieval quality: chunks that are too large dilute relevance (a search may match a chunk mostly because of one line buried in it), while chunks that are too small lose surrounding context needed to answer a question correctly.
This tool previews three common chunking strategies: fixed character count with optional overlap (simple, size-guaranteed, but can cut mid-sentence), sentence-boundary grouping (keeps sentences intact, sizes vary), and paragraph-boundary grouping (keeps larger semantic units intact, coarser granularity).
When to use it
Reach for this before feeding documents into a RAG pipeline, vector database, or any workflow that pastes long content into an LLM prompt piecemeal — you want to see actual chunk boundaries and sizes before committing to an ingestion pipeline's parameters. It's also useful for debugging why a RAG system returns awkward or incomplete answers: often the chunking strategy split a key fact across two chunks, so neither retrieval result contains the full answer.
Overlap (only applicable to the character-based method here) helps mitigate boundary loss by repeating a slice of trailing text at the start of the next chunk, at the cost of some redundant storage and retrieval duplication.
Example
Paste a 2,000-character support article, choose "sentences" with chunk size 500, and the tool groups whole sentences until adding the next one would exceed 500 characters, then starts a new chunk — so you get roughly 4 chunks, each ending on a full sentence rather than mid-word. Switch to "chars" with the same size and a 50-character overlap to see the same text sliced at fixed boundaries instead, useful for comparing against a RAG pipeline that only supports fixed-size chunking.
How to use the AI Content Chunking Preview tool
- 1
Paste your content
Drop the document or article text you plan to feed into an LLM or RAG pipeline.
- 2
Pick a chunking method
Choose chars (fixed size, optional overlap), sentences, or paragraphs as the split boundary.
- 3
Set chunk size (and overlap)
Match the target size to your embedding model or context window budget; overlap only applies to the chars method.
- 4
Review the chunks
Check chunk count, average size, and whether boundaries land in sensible places before wiring this into a real pipeline.
Frequently asked questions
Which chunking method should I use?
Sentence or paragraph boundaries generally produce more coherent chunks for RAG retrieval since they avoid cutting a thought in half. Fixed character chunking is simpler and guarantees predictable sizes, which some vector-store or embedding-model pipelines require, at the cost of occasionally splitting mid-sentence.
What does overlap actually do, and why is it only for character chunking?
Overlap repeats the trailing N characters of one chunk at the start of the next, so a fact split across a boundary still appears whole in at least one chunk. Sentence and paragraph modes already chunk on natural boundaries, so this tool doesn't apply character overlap to them — overlap there would mean repeating whole sentences/paragraphs, which most RAG setups handle differently.
What chunk size should I pick?
It depends on your embedding model's practical limits and your retrieval strategy — common ranges are 200-500 characters for tight semantic search and 500-1500 for broader context, but there's no universal number. Test retrieval quality with your actual queries rather than guessing.
Is my content uploaded to train or process anywhere?
No. All chunking logic runs locally in your browser with plain string operations — nothing is sent to a server or any AI model.
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