Use Retrieval Augmented Generation (RAG) in Data 360 to improve large language model (LLM) responses for agents and users. RAG is a framework for grounding prompts with relevant, accurate knowledge from unstructured data sources.
- About Retrieval Augmented Generation
Learn how Retrieval Augmented Generation (RAG) in Data 360 enhances your Agentforce and Einstein generative AI solutions with knowledge. - Example: Agentic RAG with Advanced Data 360 Setup
This example walks you through the end-to-end steps to implement an agent that's grounded with knowledge using Retrieval Augmented Generation (RAG) in Data 360. - Troubleshooting Knowledge Retrieval for Agents
Solve common retrieval augmented generation (RAG) issues for agents.
See Also
- Salesforce Help (Data 360): Search for AI, Automation, and Analytics
- Salesforce Help: Manage Retrievers in AI Models (formerly Einstein Studio)
- Ground with Knowledge Using Retrieval Augmented Generation
- Trailhead: Retrieval Augmented Generation: Quick Look
- Trailhead: Advanced RAG with Data 360 and Agentforce
- Video: Improve Agentforce with RAG