๐ ๐ ๐ฅ๐๐ ๐ช๐ฎ๐ ๐๐ฟ๐ผ๐ธ๐ฒ๐ป. ๐ง๐ต๐ฒ ๐ฃ๐ฟ๐ผ๐ฏ๐น๐ฒ๐บ ๐ช๐ฎ๐ ๐๐ต๐๐ป๐ธ๐ถ๐ป๐ด.
I thought RAG was hard because of embeddings. I thought vector databases were the issue. I thought the LLM was the problem. I was wrong.
The system returned results. The LLM gave answers. But the answers were wrong. They lacked context. They were irrelevant.
The problem was document splitting. RAG systems find what you provide. Bad chunking causes:
- Missing context
- Poor retrieval
- Hallucinations
Large chunks add noise. One 20-page chapter as one chunk is too much. Small chunks break meaning. Splitting a sentence in half kills the point.
Use overlap. Overlap keeps information across boundaries. Avoid fixed character counts. Text does not fit into 500-character blocks.
Use document structure:
- Documentation: Use sections.
- Code: Use functions.
- Contracts: Use clauses.
Chunking is retrieval engineering. It determines answer quality. Better chunks lead to:
- Better retrieval
- Better context
- Better answers
Fix your chunks before you change your model.
Source: https://dev.to/threshika_vs/i-thought-my-rag-was-broken-the-real-problem-was-chunking-4b04 Optional learning community: https://t.me/GyaanSetuAi