RAG

💬 In plain words: If your search engine pulls the wrong document chunks, nothing downstream can save your AI's answer. No amount of clever prompt engineering, no larger Large Language Model (LLM), and no strict system instructions will fix it. Retrieval quality sets the absolute mathematical maximum for your answer quality.

💬 In plain words: Retrieval-Augmented Generation (RAG) sounds complicated, but it operates in four basic steps: cut a long document into smaller pieces, turn each piece into math numbers (vectors) that capture its meaning, organize those numbers in a search index, and then match a user's question against them to find the perfect answer.

⚡ Key Points: 1-Minute Summary AI hallucination happens when an AI generates information that sounds completely real but is factually incorrect. Large Language Models ( LLMs ) predict responses based on language patterns, meaning they do not automatically fact-check themselves. Missing business context is the number one cause of wrong AI answers in the workplace. Enterprise AI (like Salesforce's Einstein Trust Layer) stops hallucinations through data grounding, strict permissions, and retrieval techniques. Integrated setups (like Claudeforce or Agentforce) make models like Claude highly reliable by feeding them real, live CRM data before they answer. 💬 In plain words: AI hallucination is exactly like a person who wants to be helpful, so they give you a highly confident answer without actually knowing the facts. The AI isn't intentionally lying to you; it's just trying to fill in the blanks using patterns i...

💬 In plain words: An AI model knows absolutely nothing about your specific company. Grounding is how you securely inject real Salesforce facts into the prompt right before the AI answers. There are five ways to do this in Agentforce, ranging from grabbing a simple field to searching a massive Data Cloud document.