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The advent of large language models (LLMs) has unlocked new use cases for interacting with internal data through natural language queries, known as "retrieval-augmented generation" (RAG). However, deploying production-grade RAG systems presents challenges, from engineering to operational hurdles.

This white paper outlines best practices for addressing key challenges in developing and deploying RAG systems, including:

• Efficient retrieval using vector databases for semantic search and relevance
• Choosing the right LLM model and using prompt engineering and guardrail techniques to improve generation quality
• And more

Read on now to find out how you can set up a production-ready RAG system.