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

Multi-stage and Agentic RAG Patterns

Discover advanced RAG architectures that involve multiple retrieval steps or integrate with LLM agents for complex tasks.

Complex Queries & Advanced RAG

Welcome to Advanced RAG! So far, we've explored basic Retrieval Augmented Generation (RAG) where an LLM answers a query using a single set of retrieved documents.

However, real-world questions can be complex. They might involve multiple parts, require reasoning, or need to interact with different systems.

Simple RAG struggles with these challenges. That's why we need more sophisticated approaches like Multi-stage RAG and Agentic RAG.

Deconstructing Multi-stage RAG

Multi-stage RAG is an advanced pattern designed to handle complex queries by breaking them down into smaller, manageable parts. Instead of one big retrieval, it performs several targeted retrievals.

Think of it like a detective solving a case: they don't just look for one clue; they follow leads, gather more information, and piece it all together step-by-step.

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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