Chunking
Splitting a document before embedding — chunk size directly determines retrieval precision.
TL;DR
Splitting documents into segments before embedding (e.g. 512 tokens).
Quick facts
- Category
- AI & knowledge management
- Product tie-in
- KobiGPT RAG platform
- Related
- See compare and tools pages
- Locale
- TR and EN site
Why teams choose KobiGPT
- Understand terms before evaluating vendors.
- Link concepts to KobiGPT features (RAG, Kobi Kredi).
- Share glossary links with procurement and legal.
- Explore assistant use cases next.
Product facts
- Ücretsiz plan
- 100 doküman · 2 departman · 120 Kobi/ay(PLAN_CONFIG)
- Starter
- 1000 doküman · 5 departman · 1000 Kobi/ay(PLAN_CONFIG)
- Pro
- 12500 doküman · 25 departman · 12500 Kobi/ay(PLAN_CONFIG)
Why documents are chunked
An embedding vector has a fixed length and stays the same size no matter how much text is fed in. Compressing an 80-page procedure into a single vector represents that document's "general flavour" but makes finding a specific clause inside it impossible.
Chunking solves this. The document is split into meaningful units, each represented by its own vector, and only the relevant unit is retrieved at query time. That is how the right paragraph can be extracted from an 80-page document.
The chunk-size trade-off
Small chunks improve precision but break context: if the conditional sentence preceding a clause falls into a different chunk, the answer is incomplete. Large chunks preserve context but carry irrelevant text, blurring the vector representation and lowering retrieval precision.
The approach usually preferred in practice is medium-sized chunks with overlap between them. Overlap ensures sentences at a boundary appear in both chunks and reduces the risk of breakage.
The effect of document structure
Well-structured documents chunk better. A procedure with headings and numbered clauses offers natural boundaries; a wall of text does not. This is one of the concrete points where document quality turns into retrieval quality.
Scanned PDFs add a layer: without a text layer, text extraction is required before chunking. For visually dominated documents, text-based chunking may be insufficient and the visual content needs separate handling.
Applying Why documents are chunked in a controlled workflow
A useful way to evaluate chunking is to follow one real question from the source document to the final answer. Record which file was selected, what context reached the model, and what a reviewer would need to verify. This turns a definition into an operational check and makes the result comparable across teams.
The same check should include ownership and change management. Decide who updates the relevant documents, how an outdated result is reported, and which access boundary applies. KobiGPT can provide the assistant and the cited document context, but the organisation still owns the source material, permissions, and the decision made from the answer.
FAQ
What is Chunking in practice?
Splitting documents into segments before embedding (e.g. 512 tokens).
Does KobiGPT use this?
See product docs and feature pages for implementation details.
More reading?
Visit our blog and FAQ.
Accuracy disclaimer?
Educational content; verify for compliance decisions.
Can we tune the chunk size?
The chunking pipeline is managed system-side. Improving document structure — using headings and numbered clauses — affects the outcome more.
How are very long tables handled?
Tables are extracted as text and chunked. For fast-changing tabular data, the source system is preferable to a document index.
Comparison
| Feature | KobiGPT | Alternative |
|---|---|---|
| SME focus | Yes | N/A |
| Citations | When using RAG | N/A |
| Glossary depth | Growing | N/A |
| Tools | Interactive | N/A |