Glossary
Chunking
Splitting documents into passages small enough to retrieve precisely but large enough to stay meaningful.
Chunk too small and passages lose the context that made them answerable. Chunk too large and retrieval returns mostly irrelevant text, diluting the answer.
Structure-aware chunking, respecting sections, tables and headings, consistently outperforms fixed character counts.
A practical starting point is to chunk on the document's own structure, sections, clauses, table rows, and overlap adjacent chunks slightly so a sentence spanning a boundary is not lost to both. Then measure. Retrieval precision on a labelled question set will tell you within a day whether the strategy fits the corpus, which is faster than arguing about it.
Commonly misunderstood: Chunking is usually treated as a configuration value. On real corpora it is one of the highest-leverage decisions in the whole pipeline.
Related terms, in context
The concepts you almost always meet alongside chunking.
- RAG
- Retrieving relevant passages from your own documents and giving them to the model, so answers are grounded and citable.
- Embedding
- A numeric vector representing text, images or audio, positioned so similar things sit close together.
- Vector database
- A store for embeddings that answers 'what is semantically closest to this?' quickly.
Where this shows up in our work
Chunking is not an abstraction for us. It is a decision we make on live projects. It shows up most directly in rag & knowledge retrieval, where getting it wrong has a cost someone can measure.
If you are evaluating a vendor on this, the useful question is not whether they can define the term. It is what they measure, what they would refuse to do, and what happens in their system when the assumption behind chunking stops holding.
Questions
What is Chunking?
Splitting documents into passages small enough to retrieve precisely but large enough to stay meaningful.
What do people get wrong about chunking?
Chunking is usually treated as a configuration value. On real corpora it is one of the highest-leverage decisions in the whole pipeline.
Does Orqent Labs build this?
Yes, RAG & Knowledge Retrieval Systems. We work across India, covering all 19,238 PIN codes remotely.
Building something that involves chunking?
We will tell you honestly whether it is the right approach for your problem.
Or email bd@dtrasglobal.com · call +91 74118 77878