GPTfy Glossary
Grounding
Supplying an LLM with authoritative, current, customer-specific data inside the prompt so its response is anchored in real information, not training data.
Grounding is the antidote to hallucination. Without grounding, an LLM answers from its training data — which may be months or years out of date and contains zero knowledge of your customers, products, or processes. With grounding, you inject the relevant facts into the prompt: account history, latest policy docs, current pricing.
In Salesforce, grounding sources typically include: Account/Contact/Case records, Knowledge Articles, Opportunity history, related documents in Files or Content. The Einstein Trust Layer and platforms like gptfy automate grounding by retrieving relevant records (via RAG), formatting them into a system prompt, and ensuring the LLM only answers from grounded context.
A well-grounded prompt dramatically reduces hallucination but increases token cost (more context = more tokens). The art is in retrieval — pulling exactly the relevant grounding without flooding the prompt.
See Grounding in GPTfy
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