RAG Without the Plumbing: Agentforce Data Library

The scenario

Falcon Telecom's support answers live in ~400 knowledge articles and PDF product manuals. Their agent keeps saying "I don't have that information" — or worse, guessing. The fix is RAG (Retrieval-Augmented Generation): before answering, the agent retrieves the most relevant chunks of real content and grounds its response in them.

For beginners: think of RAG as an open-book exam. Instead of the model answering from memory (and hallucinating), it first looks up the right page, then answers from what it found.

What Data Library automates for you

Building RAG by hand means data pipelines, chunking logic, embeddings, a vector store, a search index, a retriever and a prompt that stitches it together. Agentforce Data Library (powered by Data Cloud) creates all of that automatically:

• Pick a data source — Salesforce Knowledge (KnowledgeArticleVersion) or uploaded files (PDFs, docs).
• Choose the key fields (Title, Summary, Article Body).
• Data Cloud auto-creates the chunk object, the index object, the search index and a retriever.
• The library wires an "Answer Questions with Knowledge" action to your agent — retrieval included.

Build it: step by step

1. Enable Data Cloud (included with Agentforce Developer Edition orgs — free to learn on).
2. In Setup, open Agentforce Data Library → New. Name it Support Knowledge.
3. Select the Knowledge source and confirm the fields to index. Publishing kicks off ingestion, chunking and index build.
4. Open your service agent in Builder, and on the knowledge topic attach the library's Answer Questions with Knowledge action.
5. Test with a real question from your case logs: "How do I return my router?" — the agent now answers from the actual article and cites it.

Going custom: your own retriever in a prompt template

For finer control, Prompt Builder can call a retriever directly inside a template, so any Flow, Apex entry point or agent action can use grounded generation:

You are a support assistant for Falcon Telecom.
Answer the customer's question using ONLY the retrieved passages below.
If the answer is not in the passages, say you don't know.

Question: {!$Input:customer_question}

Retrieved passages:
{!$EinsteinSearch:Support_Knowledge_Retriever.results}

The retriever fetches the top-matching chunks from the search index at run time; the Einstein Trust Layer masks sensitive data before anything reaches the LLM.

What you learned

• RAG stops hallucinations by grounding answers in retrieved, trusted content.
• Data Library gives you the whole retrieval pipeline — chunks, index, retriever, action — with clicks.
• Instruct the model to answer only from retrieved passages and to admit when it doesn't know.

Sources: Salesforce Engineering — How Agentforce Data Library powers RAG · Agentforce and RAG best practices