Integrating RAG and Unstructured Data in Agentforce

AI agents are only as good as the data they can access, yet most business information is locked away in unstructured formats like documents, emails, and knowledge articles. This text-based course teaches you how to bridge this gap using Retrieval-Augmented Generation (RAG) within Agentforce. By understanding how to ground AI responses in your own reliable data sources, you will learn to transform generic AI interactions into highly precise, context-aware automated workflows. What you'll learn: - Understand the foundational concepts of Retrieval-Augmented Generation (RAG) and its role in AI accuracy. - Identify and prepare unstructured data sources for seamless AI ingestion. - Configure RAG pipelines to connect external data repositories directly to Agentforce. - Apply chunking and search strategies to optimize how information is retrieved by your agents. - Verify and test the grounding of your AI responses to eliminate hallucinations and ensure reliability. We begin with core terminology and the mechanics of RAG before exploring how unstructured data is processed and mapped. From there, you will read through conceptual guides on configuring retrieval paths, setting up search parameters, and validating agent performance. This course is designed for beginners, business analysts, and administrators looking to enhance AI capabilities. No prior programming or machine learning experience is required. Start reading today to unlock the power of your unstructured data and build smarter AI agents.