In this blog, we discuss the necessary considerations for running Retrieval Augmented Generation (RAG) pipelines in production.

Defining a RAG pipeline

Production considerations

RAG performance and monitoring

RAG benchmarking and evaluation

RAG logging

Creating a unified RAG data platform

An Elastic Search AI framework for RAG

Conclusion

A RAG pipeline consists of the following elements: a user-provided question, a search generated context, a prompt template, and an LLM generated answer.

What does a RAG pipeline consist of?

RAG pipelines in production: Operationalize your GenAI project

Author

Tim Brophy

Articles

RAG pipelines in production: Operationalize your GenAI project