Fix “dumb RAG” using hybrid retrieval and a lightweight reranker pipeline.
8 min read2 days ago
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If your RAG app is “kind of okay” but randomly wrong, you don’t have an LLM problem.
You have a retrieval problem.
Most “dumb RAG” fails for the same reason: it retrieves the most similar-looking text, not the most useful text. That tiny difference is why your answers feel confident… and still miss the point.
The fix is simple and powerful:
Hybrid Search RAG = BM25 (keywords) + Vectors (semantic) + Reranking (precision).
This single upgrade can make your RAG system:
- more accurate on real questions,
- faster under load,
- less hallucination-prone,
- and dramatically better with messy enterprise docs.
In this…
Fix “dumb RAG” using hybrid retrieval and a lightweight reranker pipeline.
8 min read2 days ago
–
Press enter or click to view image in full size
If your RAG app is “kind of okay” but randomly wrong, you don’t have an LLM problem.
You have a retrieval problem.
Most “dumb RAG” fails for the same reason: it retrieves the most similar-looking text, not the most useful text. That tiny difference is why your answers feel confident… and still miss the point.
The fix is simple and powerful:
Hybrid Search RAG = BM25 (keywords) + Vectors (semantic) + Reranking (precision).
This single upgrade can make your RAG system:
- more accurate on real questions,
- faster under load,
- less hallucination-prone,
- and dramatically better with messy enterprise docs.
In this article, we’ll build a Hybrid Search RAG pipeline in Python that you can actually ship.
Why “Dumb RAG” Breaks in the Real World
A typical basic pipeline looks like this:
- chunk documents
- embed chunks