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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.

BET
Botmartz Engineering 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 QualityChunking StrategyHybrid SearchContinuous EvaluationGuardrailsTakeaways
# Advanced RAG Techniques: Hybrid Search, Reranking & Query Rewriting Basic RAG systems often suffer from poor retrieval accuracy, fetching irrelevant context that leads to model hallucinations. To build high-quality production RAG, advanced retrieval techniques are necessary. ## The Advanced Retrieval Stack 1. **Query Rewriting**: Standardizing user queries before search to align with index terminology. 2. **Hybrid Retrieval**: Joining dense vector search and sparse keyword search (BM25). 3. **Reranking**: Sorting retrieved chunks using high-performance cross-encoders to bubble up the most relevant documents.

Closing Takeaways

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Measure retrieval precision and recall in isolation before touching the model.
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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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BET
Botmartz Engineering Team
AI Engineer at Botmartz, building enterprise RAG and agent systems in production. Contributing to open-source libraries.

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