# Chunking Strategies for Retrieval

> Fixed-size, recursive, structure-aware and parent-child chunking — and how to choose based on your documents and questions.

- Source: https://akshaythakare.com/notes/rag/chunking-strategies
- Topic: RAG
- Level: intermediate
- Reading time: 2 min
- Tags: rag, chunking, retrieval
- Updated: 2026-10-11

## Why chunking matters

The retriever can only return what you chunked. Too small and a chunk lacks the context to be useful; too large and its embedding averages many topics, so it matches nothing precisely.

## Strategies

| Strategy | How | Good for |
| --- | --- | --- |
| Fixed-size | N tokens with M overlap | Uniform prose, quick baselines |
| Recursive | Split on paragraphs → sentences → words until under the limit | General text |
| Structure-aware | Split on headings, sections, code blocks, table rows | Docs, wikis, Markdown, technical notes |
| Semantic | Split where embedding similarity between sentences drops | Long unstructured text |
| Parent-child | Embed small chunks, return their larger parent section | Precise matching *and* enough context |

## Structure-aware chunking for technical docs

```python
def chunk_markdown(md: str, max_tokens: int = 400):
    sections = split_on_headings(md)           # keep the heading path, e.g. "Snowflake > COPY INTO"
    for s in sections:
        for piece in recursive_split(s.body, max_tokens, keep_code_blocks_whole=True):
            yield {"text": f"{s.heading_path}\n\n{piece}", "heading": s.heading_path}
```

Prefixing each chunk with its **heading path** is one of the cheapest, most effective improvements: the chunk now carries its own context.

## Practical defaults

- Start around **300–500 tokens with 10–15% overlap**, then measure
- Never split inside a code block or a table row
- Store metadata (source, section, date, version) for filtering and citations
- Re-chunk and re-embed when you change the strategy — mixed strategies in one index are hard to reason about

> **Note: Evaluate, don't guess**
>
> Pick two or three strategies, run your question set, and compare retrieval recall at k. The right size depends on your documents and questions, not on a blog post.