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Challenges in Building RAG Systems (and Solutions Using OpenSearch) - Parminder Singh, Tempera AI

Overview

Why use RAG (Retrieval-Augmented Generation)?
How does OpenSearch contribute to building intelligent agents?

Barriers to Deploying RAG Systems in Production:

Key concerns: Quality, Cost, Safety, Maintenance, and Knowledge/Time.
The biggest challenge remains quality.
What leads to quality degradation in RAG systems, and how does it happen?

Solving the Issues:

Identifying challenges with different chunking techniques: Which one should you use, or should you develop a custom approach tailored to your needs using OpenSearch?
Addressing problems with search/querying techniques to optimize results.

By understanding these challenges and adopting the suggested solutions, you can significantly enhance the accuracy of RAG-based applications.

If needed, we can provide a deeper dive, a demo, or even hands-on workshops on this topic.