COMPEL Glossary / grounding
Grounding
Grounding refers to techniques that connect AI model outputs to factual, verifiable information sources rather than relying solely on patterns learned during training.
What this means in practice
When an AI agent is grounded, its responses are anchored in retrieved documents, database queries, or other authoritative sources rather than generated from memory alone. Grounding is the primary defense against hallucination -- the generation of plausible but incorrect information. Key grounding techniques include retrieval-augmented generation (RAG), citation verification (requiring the model to cite specific sources), knowledge cutoff awareness (acknowledging when information may be outdated), and source attribution (making clear where information comes from). For organizations deploying AI agents that make decisions or take actions based on factual claims, grounding is not optional -- it is a prerequisite for trustworthy operation.
Why it matters
Grounding is the primary defense against hallucination, which is one of the most significant operational risks of deploying generative AI. When AI outputs are anchored in retrieved documents, database queries, and authoritative sources rather than generated from memory alone, accuracy and trustworthiness improve dramatically. For organizations deploying AI agents that make decisions based on factual claims, grounding is a prerequisite for trustworthy operation.
How COMPEL uses it
During Model, grounding requirements are designed into AI system architectures, specifying which authoritative sources each system must reference. The Technology pillar design includes RAG architecture, citation verification, and source attribution infrastructure. The Produce stage implements grounding mechanisms, and the Evaluate stage measures grounding effectiveness through hallucination rate tracking and source accuracy audits.
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