Querying and Generating Answers
Develop the logic for processing user queries, retrieving relevant context, and synthesizing answers using the LLM.
Querying RAG: The Answer Flow
After integrating RAG components, the next step is to use them to answer user questions. This lesson covers the full process from a user's query to a generated answer.
We'll focus on the 'query-time' logic: how your system takes a question, finds relevant context, and synthesizes a coherent response using an LLM.
Understanding the User Query
A RAG system starts with a user's question, just like a search engine. This raw input is the trigger for the entire process.
- It defines what information needs to be retrieved.
- It guides the LLM on what kind of answer to generate.
No special formatting is typically needed at this initial stage; it's just plain text.
All lessons in this course
- Integrating All RAG Components
- Querying and Generating Answers
- Evaluating RAG System Performance
- Building a Golden Test Set for RAG