What is Retrieval Augmented Generation (RAG) and how the technique can help improve the quality of an LLM's generated responses, by providing relevant source knowledge as context.

Challenges with Generative AI

Lack of Domain Knowledge

Frozen Parametric Knowledge

Hallucinations

Expensive to Train

How does RAG help solve this problem?

How does RAG work?

RAG Pipeline Flow

Retrieval of source knowledge

Advantages of RAG

Grounded in Source Knowledge

Citing Sources in Responses

RAG in Action

Build a Context

Prompt Template

Question

Challenges with RAG

Precise Dense Vectors

Large Context

Conclusion

Retrieval Augmented Generation (RAG)

Author

Joe McElroy

Articles

Retrieval Augmented Generation (RAG)