A concrete example
I split a 200-page PDF into 500-token chunks. Each chunk becomes searchable, and I only feed back the relevant ones.
Why it matters
Bad chunking = bad RAG. It's the invisible move that makes or breaks the quality of your whole search.
It's a key step when you build a RAG on top of PDFs, pages, or knowledge bases.
Don't mix it up with
RAG: RAG lets an AI answer using documents pulled up at the moment you ask the question.
Chunk overlap: Chunk overlap keeps a small shared zone between two pieces of a document so an idea isn't cut off in the wrong place.
Common mistakes
- Making chunks too big: search loses precision.
- Making chunks too small: the context gets broken.
- Ignoring the overlap between chunks (paragraphs cut off mid-sentence).
Quick checklist
- First I check whether the word names a concept, a tool, a risk, or a metric.
- I tie it to a concrete case: I split a 200-page PDF into 500-token chunks. Each chunk becomes searchable, and I only feed back the relevant ones.
- I keep the main trap in mind: Making chunks too big: search loses precision.
Quick questions
What is Chunking in AI?
Chunking splits a long document into pieces an AI search system can actually work with.
Where will I run into Chunking?
It's a key step when you build a RAG on top of PDFs, pages, or knowledge bases.
Which word should I read next?
Start with RAG, Chunk overlap, Embedding.