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What is grounding?
Grounding is the technique of basing AI responses on reliable, verifiable sources. Instead of a model responding solely based on its training data, it is provided with relevant facts from corporate knowledge, current databases, or other sources at runtime.
The term comes from AI terminology: “grounded” means “based on something concrete”—as opposed to made-up statements. Grounding is thus the key solution to the hallucination problem.
Techniques: RAG (Retrieval-Augmented Generation) is the most widely used approach. Grounding is often supplemented by mandatory citations, guardrails (“only respond if a source is available”), structured output, and chain-of-verification.
For businesses, grounding is the key to moving generative AI from the prototype stage to reliable production use—in customer service, knowledge assistants, and regulated applications.
Best Practices for Grounding
These six principles help with productive grounding:
- Curate sources: Better results come from a few good sources than from a chaotic mess of junk data.
- Require citations in the prompt: The model should cite the source for every statement—this is technically enforceable.
- Clear fallbacks: “I don’t know” is better than making something up—allow it as a valid response.
- Test the Golden Set: Collect realistic questions and measure grounding success.
- Maintain the knowledge base: This isn’t a one-time effort—ongoing updates are mandatory.
- Raise user awareness: Make citations visible—users can verify them themselves.
Common Mistakes in Grounding
We see these pitfalls time and time again:
- Unfiltered data clutter: Throwing all SharePoint documents into the mix — poor retrieval quality.
- Citation only in the prompt: Without technical verification, the model often ignores the citation instruction.
- No fallback: The model makes things up when no source matches—undermining grounding.
- No evaluation: Grounding success isn’t measured—poor results go undetected.
- Knowledge base becomes stale: No update processes—answers become outdated.
Grounding vs. RAG vs. Fine-Tuning
Three related concepts:
- Grounding: The goal — to base answers on reliable sources.
- RAG: The most common technique for implementing grounding. Combines retrieval and generation.
- Fine-Tuning: The model is adapted—not grounding in the strict sense, but rather knowledge internalization.
Grounding for Your AI Applications
In a free initial consultation, we’ll assess the grounding maturity of your AI applications and identify critical areas for improvement—including a concrete implementation proposal.
As an AI partner for small and medium-sized businesses, we put grounding to productive use—with RAG, citation requirements, and a verification layer.
What We Offer
- RAG in the glossary —the most common grounding technique.
- AI Agents for Businesses — Grounding as a Core Component.
- AI monitoring —measuring grounding success in operation.
- Hallucination in the glossary —the problem that grounding solves.
