Augment your AI agents using RAG

GenAI and agents are taking the world by storm. But one of the first challenges organizations face when deploying AI agents is ensuring LLMs can understand proprietary enterprise data.

Retrieval augmented generation (RAG) is a proven technique for enhancing LLMs with enterprise data. To ensure LLM-powered agents respond accurately, companies use RAG to provide domain-specific knowledge from user manuals and support documents.

This compact guide dives deep into architecture, implementation best practices and how to evaluate GenAI application performance.

You’ll learn:

• The fundamentals of RAG, including the different types of applications
• Key components of RAG — retrieval and generation — and the implementation choices and configurations you can use
• How to evaluate and properly understand the accuracy of your RAG application
• How to implement RAG and the features needed to support a productionized application