What is Retrieval-Augmented Generation (RAG) and how does Data Cloud enable it for Agentforce?

Retrieval-Augmented Generation (RAG) is an AI architecture pattern that improves LLM accuracy by retrieving relevant context from an external data store and injecting it into the prompt before generation. This grounds the LLM's response in factual, up-to-date data rather than relying solely on its training data. Data Cloud as a Vector Database: Data Cloud stores data as vector embeddings — high-dimensional numerical representations of text that capture semantic meaning. When an agent receives a user query, the query is also vectorised and a semantic search (cosine similarity lookup) finds the most contextually relevant data chunks. These chunks are injected into a Grounding Prompt Template. Key concepts:

1. Chunking: Source documents are split into smaller segments (chunks) before vectorisation to stay within context window limits and improve retrieval precision. Chunk size is a tuning parameter.
2. Grounding: The retrieved chunks form the factual basis for the LLM's response, dramatically reducing hallucination on domain-specific topics.
3. Data Cloud as Memory: Unified customer profiles and interaction history in Data Cloud can be retrieved to personalise agent responses with full customer context.