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Salesforce demonstrates how grounding AI agents in curated product knowledge via retrieval augmented generation (RAG) is critical for answer accuracy and trustworthiness. The company's approach prioritizes content quality metrics like "fit," "gap," and "clash" over simple volume, noting that unfiltered large indexes can degrade agent performance. This case study highlights the operational necessity of rigorous data preparation pipelines to ensure AI systems resolve user queries effectively without hallucination.

Sources coveringSalesforce Blog, Latent Space, arXiv, LessWrong

4 Data 360 Lessons for Connecting Product Knowledge to AI

Salesforce BlogNovember 4, 20266 min read

Learn how Salesforce uses Data 360 to ground its Help Agent in trusted content and how to assess, test, and measure your own agent's content and answers.

What does it take to trust an AI agent’s answers? For the Salesforce Help Agent, it starts with the content behind it. We use Data 360 to prepare and organize our product knowledge, and Agentforce puts it to work for our customers. This is grounding: connecting an agent to your own content so its answers are sourced from your trusted information. When a customer asks a question, an agent retrieves the relevant passages and builds its response using retrieval augmented generation (RAG). This means your agent can only be as good as the content it retrieves. Here are four lessons from our Customer Zero journey, where we’re the first customer of our own products, on…

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