RAG can fail before the AI model ever sees the question
A can look accurate on clean test documents and then become quietly wrong on real documents. The visible problem may look like weak AI answers, but the real cause can sit earlier in the pipeline. The first weak point is document and chunking.
If document formats are inconsistent, chunks split ideas in the middle, or little metadata is saved, can pull the wrong pieces of text. In that case, the first place to check is how documents were loaded and split, not the . The second weak point is and vector storage.
The , search settings, and decide whether the right information reaches the AI model at all. If chunks have no metadata, there is no good way to filter answers by source or freshness later.
Key points
- A RAG setup can pass clean tests but fail on messy real documents.
- Bad chunking can split meaning and make pull irrelevant text.
- When looks wrong, check and chunking before blaming the .
- Missing metadata makes it hard to filter by source or freshness.
- The , search, and vector storage shape what information reaches the AI model.