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LLM Apps in Production (RAG + Vector DB + Caching) · Lesson

Query Rewriting and Reranking

Explore techniques to optimize user queries and rerank retrieved documents for higher relevance to the LLM.

Optimizing Queries for RAG

Welcome to advanced RAG techniques! In this lesson, we'll explore two powerful methods to make your Retrieval Augmented Generation (RAG) system even smarter: Query Rewriting and Reranking.

These techniques help ensure your LLM gets the most relevant information possible, leading to better and more accurate responses.

Why Raw Queries Fall Short

When a user asks a question, their initial query might not be perfect for searching your knowledge base. It could be:

  • Too short or vague: Lacking specific keywords.
  • Ambiguous: Having multiple possible meanings.
  • Missing synonyms: Not using the exact terms found in your documents.

This can lead to your retriever fetching less relevant documents.

All lessons in this course

  1. Query Rewriting and Reranking
  2. Multi-stage and Agentic RAG Patterns
  3. Handling Complex Document Structures
  4. Self-Querying & Citations
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