Retrieval Augmented Generation enables GenAI the ability to answer questions using information that was not part of the model's training dataset, unlocking significant increases in productivity and user experience. In this blog we discuss the considerations necessary to run 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

Retrieval Augmented Generation in production: Operationalize your GenAI project

RAG in production: Operationalize your GenAI project

Author

Tim Brophy

Articles

RAG in production: Operationalize your GenAI project