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Advanced RAG Techniques: Hybrid Search, Rerank in g & Query Rewriting

Improve retrieval accuracy and reduce hallucinations with hybrid search, rerankers, and LLM-based query rewriting.

BT

Botmartz Team

AI Engineer, Botmartz · April 25, 2024 · 10 min read

◈ Open in Google Colab ⌥ View Code on GitHub

Read Time

10 min

Failure Modes

5

Code Snippets

3

Runnable Notebook

1

Botmartz AI Insight

Evaluating Retrieval, Chunking, and Generation in Production

Retrieval Quality Chunking Strategy Hybrid Search Continuous Evaluation Guardrails Takeaways

# Advanced RAG Techniques: Hybrid Search, Reranking & Query Rewriting Basic dense vector retrieval often fails on exact keyword matches, SKU numbers, or domain-specific acronyms. Advanced RAG combines multiple retrieval techniques to dramatically boost precision. ## Key Techniques - **Hybrid Search**: Combining sparse BM25 indexing with dense embedding cosine similarity. - **Cross-Encoder Reranking**: Passing retrieved candidate chunks through a cross-encoder model to accurately re-score context relevance. - **Query Rewriting (HyDE)**: Using an LLM to expand or transform ambiguous user queries before searching the vector space.

Closing Takeaways

✓

Measure retrieval precision and recall in isolation before touching the model.

✓

Chunk along document structure, not arbitrary character counts.

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Combine vector and keyword search — hybrid retrieval beats either alone.

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Treat evaluation as continuous infrastructure, not a launch-week report.

Try It Yourself

A runnable Google Colab notebook with the eval harness and hybrid search code from this post.

◈ Open in Colab ⌥ GitHub Repo

#Enterprise RAG#Evaluation#Production AI#LangChain

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BT

Botmartz Team

AI Engineer at Botmartz, building enterprise RAG and agent systems in production. Contributing to open-source libraries.

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