Next Steps

In this module, we covered hybrid search and document reranking, two of the most impactful techniques for improving RAG quality.

The key takeaways:

• Hybrid search combines vector and keyword search for better retrieval. It works because vector search captures semantic meaning while keyword search catches exact term matches.
• Reranking re-orders results with a better relevance score. Even a simple RRF implementation can improve metrics.
• LangChain wraps Elasticsearch functionality in a cleaner interface, but the underlying queries are the same.

Other techniques to explore

We only covered two of the five techniques from the intro lesson.

Here are pointers for the rest:

• Small-to-big chunk retrieval: use small chunks for embedding but retrieve surrounding context for the LLM. Look at LangChain's ParentDocumentRetriever.
• Document metadata: add titles, topics, dates as filterable fields in your search index. Use them to narrow results before ranking.
• Query rewriting: ask an LLM to reformulate the user's question into a clearer search query before hitting the database.

To learn more

Check out these resources:

• Advanced RAG techniques overview
• LangChain retrieval docs

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