AI RAG Tutorial: Hybrid Search, Rerank

TL;DRBetter RAG combines keyword and vector search (hybrid) and reranks results for relevance.

Pure vector search misses exact terms (names, codes); pure keyword search misses meaning. Hybrid search blends both, then a reranker (a model that scores query-document relevance precisely) reorders the top candidates so the best context lands in the prompt. These two upgrades — hybrid retrieval and reranking — are the highest-leverage fixes when a RAG system returns mediocre answers.

Key points

Hybrid = keyword + vector search
Reranker re-scores top candidates precisely
Best context → into the prompt
Top fixes for mediocre RAG

Common mistakes

Vector-only search missing exact terms
Skipping a reranker on noisy retrieval
Stuffing all candidates instead of the best

Try it: Explain what a reranker adds on top of initial retrieval.

Example code

<!doctype html><html><head><meta charset="utf-8"></head>
<body style="background:#06040d;color:#e6e0ff;font-family:monospace;padding:20px"><pre>retrieve (hybrid: keyword + vector) → 50 candidates
rerank by precise relevance → top 5 → prompt</pre></body></html>

Open the interactive lesson →

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