RAG Chunking Strategies: Split Documents for Better Retrieval
Most RAG failures happen because of how you split your documents.
If your retrieval is poor, do not change your prompt or your LLM first. Look at your chunks. If the correct information is in your database but the system cannot find it, your chunking strategy is likely the problem.
Bad chunking causes three main issues:
• Boundary truncation: A sentence with the answer gets split into two pieces. Neither piece has enough info to match a query. • Context dilution: A large chunk has one relevant sentence and ten useless ones. The extra text weakens the semantic signal. • Missing metadata: Chunks lack info about their source or date, making filtered search impossible.
Use these four strategies to fix your pipeline:
Fixed-size chunking Best for long, continuous prose like reports or articles. • Use 256 to 512 tokens. • Set a 10% to 15% overlap to prevent split sentences.
Semantic chunking Best for high-density text like FAQs or support docs. • It splits text based on topic shifts rather than token counts. • This keeps complete ideas together.
Structural chunking Best for technical docs, Markdown, or HTML. • It splits text based on headers (H1, H2, H3). • This adds metadata so you can filter retrieval by section.
Hierarchical (Parent-Child) chunking Best for production systems needing both precision and context. • Create small child chunks (64-128 tokens) for precise vector search. • Link them to large parent chunks (512-1024 tokens) for the LLM to read. • This gives you the best of both worlds.
How to choose your size:
• 128–256 tokens: Good for fact-lookup and technical docs. • 256–512 tokens: A solid starting point for general use. • 512–1024 tokens: Use for long-form analytical questions.
The golden rule: Always test your strategy before you ship.
Build a set of 30 to 50 real queries. Annotate the correct answers. Measure your recall@3. Do not change your embedding model until your recall is above 80%.
Optional learning community: https://t.me/GyaanSetuAi