10.4 Advanced RAG Patterns

While basic Retrieval-Augmented Generation (RAG) is powerful, its performance can be significantly enhanced by more sophisticated patterns. These advanced techniques address challenges like improving retrieval accuracy, synthesizing information from multiple sources, and making the generation process more robust.

Interactive RAG Pattern Explorer

Click on a node in the diagram below to learn more about each advanced RAG pattern. The diagram illustrates how a user query can be processed through different enhancement stages.

1. Query Transformations

The quality of retrieval is highly dependent on the input query. Query transformations refine the user's prompt to be more effective for vector search.

• HyDE (Hypothetical Document Embeddings): Generates a hypothetical document to create a more contextually relevant vector for searching.
• Multi-Query Retriever: Generates multiple variations of the user's query to broaden the search and find more relevant documents.

2. Routing and Logic

A single RAG pipeline may not be optimal for all queries. A router can decide which data source or retrieval strategy to use based on the user's intent.

• Semantic Router: An LLM classifies the query to route it to the appropriate knowledge base (e.g., general vs. technical).
• Tool-Using Agents: Integrates RAG into a larger agentic framework, allowing the agent to decide when to use the RAG tool.

3. Advanced Indexing and Retrieval

Improving the structure of indexed data is crucial.

• Parent Document Retriever: Retrieves larger parent documents for more context, even when the match is in a small chunk.
• Self-Querying Retriever: Enables the LLM to write its own metadata filters for the vector store, combining semantic search with structured filtering.