What is Retrieval Augmented Generation?

RAG grounds AI outputs in real data retrieved from enterprise sources.

What problem does RAG solve?

RAG provides real data to LLMs, reducing hallucination and improving relevance.

What data sources can RAG use?

RAG can draw from multiple enterprise data sources for comprehensive context.

Retrieval Augmented Generation

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Definition

In Salesforce Einstein and Data Cloud, a technique where the AI retrieves relevant data from your CRM, knowledge base, or Data Cloud before generating a response, grounding the output in your actual business data.

Real-World Example

a data scientist at CognitiveTech uses Retrieval Augmented Generation to automate a complex decision-making process that used to rely on gut instinct. By deploying Retrieval Augmented Generation, the organization now uses data-driven intelligence to guide actions, resulting in better customer outcomes and more efficient use of team resources.

Why Retrieval Augmented Generation Matters

In Salesforce Einstein and Data Cloud, Retrieval Augmented Generation (RAG) is a technique where the AI retrieves relevant data from your CRM, knowledge base, or Data Cloud before generating a response, grounding the output in your actual business data. Instead of relying purely on an LLM's training knowledge (which is generic and may be stale), RAG looks up relevant information from your systems and provides it to the LLM as context when generating output.

RAG is foundational to practical enterprise AI because it solves the hallucination and relevance problems that affect pure LLM approaches. Without RAG, an AI assistant answering questions about your customers would either guess (risking wrong answers) or admit ignorance (not useful). With RAG, the AI has access to actual customer data when generating responses, producing outputs that are both relevant and grounded. Mature Salesforce AI deployments use RAG extensively, combining LLMs with enterprise data for grounded outputs.

How Organizations Use Retrieval Augmented Generation

•CloudNine Solutions — Uses RAG to ground AI-generated service responses in actual customer case history and knowledge articles.
•TrueNorth Software — Implements RAG in their sales assistant to produce responses grounded in real opportunity and account data.
•Apex Analytics — Treats RAG as foundational for enterprise AI, ensuring AI outputs reflect real business data.