Learn how Retrieval‑Augmented Generation (RAG) makes AI answers in Salesforce more accurate by grounding LLM responses in your company data — and simple steps to get started with Agentforce and Data 360.
What is RAG?
Retrieval‑Augmented Generation (RAG) is a pattern that combines search and large language models so AI answers are grounded in your organization’s data. Instead of relying solely on a pre-trained model, RAG retrieves relevant documents, case history, knowledge articles, or files and passes that context to the LLM so the generated answer is factual, timely, and aligned with internal policies.
How RAG works in Salesforce
The RAG flow typically follows three stages:
- Retrieve: Search your data library (Knowledge, files, emails, PDFs, past cases) for relevant content.
- Augment: Add the retrieved snippets to the original user query to create a context‑rich prompt.
- Generate: The LLM answers using the augmented prompt, producing a response grounded in your data.
Salesforce implements this via Agentforce Builder and the Agentforce Data Library. Admins decide which content sources to include and the agent uses the “Answer Questions with Knowledge” action to fetch and attach relevant context before invoking the LLM.
Common RAG approaches
- Vector‑based RAG: Converts text into vectors for semantic similarity search.
- Knowledge graphs: Links concepts into nodes and edges for relationship‑aware retrieval.
- Ensemble RAG: Combines multiple retrievers to cross‑check and verify results.
Step‑by‑step: How to start using RAG in your org
- Ingest content from Salesforce Knowledge, files, emails, and other internal sources.
- Chunk large documents into smaller passages and vectorize them for semantic search.
- Create search indexes and configure retrievers for fast, relevant retrieval.
- Set data access controls so agents only use authorized sources.
- Deploy Agentforce AI agents that use “Answer Questions with Knowledge” to add context before generation.
Benefits
- Timely, accurate answers that reflect current internal information.
- Fewer hallucinations — the model cites company data rather than guessing.
- Administrative control over which sources the AI can access.
- Improved search and faster resolution for service, sales, marketing, and commerce teams.
Best practices
- Start small: index a few high‑value knowledge categories or case histories first.
- Use chunking and metadata to improve retrieval precision.
- Monitor responses and iterate prompt templates and retriever settings.
- Apply role‑based access control to protect sensitive content.
Conclusion
RAG transforms LLMs in Salesforce from generic chatbots into context‑aware assistants that understand your business. By combining Agentforce Builder with a disciplined data ingestion and vectorization pipeline, teams can deliver reliable AI answers — reducing manual work and increasing trust in AI outcomes.
Why this matters: For Salesforce admins and developers, RAG reduces support load and enables smarter automation. For business users, it delivers faster, accurate answers tied to company policies and product details. For leadership, it increases confidence when introducing AI into customer‑facing workflows.