Why are my retrieved passages so irrelevant in my chatbot?

Irrelevant retrieved passages in a chatbot usually come from one or more of these issues:

1. Bad chunking

   • Chunks are too large, too small, or split in awkward places.
   • The answer may be split across chunks, so retrieval gets partial context.
   • Fix: chunk by structure when possible, and tune chunk size/overlap.

2. Weak embeddings

   • The embedding model may not represent your domain well.
   • Generic embeddings can struggle with specialized vocabulary.
   • Fix: try a stronger embedding model or one better suited to your content.

3. Poor indexing strategy

   • Pure vector search may miss exact keywords, names, IDs, or acronyms.
   • Fix: use hybrid retrieval: vector + keyword/BM25.

4. Query mismatch

   • User questions may be short, vague, or use different wording than your docs.
   • Fix: rewrite/expand queries before retrieval, or use query decomposition.

5. No reranking

   • Top-k vector hits are often only “roughly related.”
   • Fix: add a reranker to sort candidate passages by actual relevance.

6. Too much noisy content

   • Tables, headers, footers, navigation text, boilerplate, or duplicated passages can dominate results.
   • Fix: clean the source text and remove repeated junk.

7. Metadata not used

   • If you have document type, date, product, section, or language metadata, ignoring it reduces precision.
   • Fix: filter retrieval by metadata when appropriate.

8. Embedding/indexing bugs

   • Wrong normalization, indexing the wrong field, stale index, or mismatched embedding models between indexing and query time.
   • Fix: verify the exact same embedding pipeline is used consistently.

9. Top-k too small or too large

   • Too small: misses relevant context.
   • Too large: includes lots of irrelevant context that confuses the model.
   • Fix: tune k and possibly use reranking.

10. The corpus simply lacks the answer

• Retrieval can only find what exists.
• Fix: check whether the right information is actually in your documents.

Quick debugging checklist

• Inspect the top 10 retrieved chunks manually for a few queries.
• Test whether exact keywords from the question appear in the retrieved text.
• Compare vector-only retrieval vs hybrid retrieval.
• Add a reranker and see if quality improves.
• Try different chunk sizes and overlaps.
• Check whether the embedding model and indexing pipeline match.
• Remove boilerplate and duplicated text.

If you want, I can help you diagnose it systematically if you share:

• your chunk size/overlap,
• embedding model,
• retriever type,
• whether you use reranking,
• and an example query with the top retrieved passages.