Glossary

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Glossary / Evaluation and implementation guide

Grounding

Grounding is the principle of binding AI outputs to verifiable source material, so a claim in an answer can be traced to evidence rather than to the model's internal patterns.

Retrieval-augmented generation (RAG) is one common implementation — pulling documents into the prompt — but grounding also covers structured data lookups and requiring citations.

The goal is that answers reflect your actual records, not plausible invention.

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A practical example

Example: when a rep asks an assistant for an account's renewal status, a grounded system answers from the CRM field with a link to the record, instead of inferring an answer from the account name alone.

What to evaluate before investing

  • Ask whether answers can include citations or source links, and whether you can require them for sensitive topics.
  • Test with a question whose answer is absent from your sources — does the system say so, or produce a confident guess?
  • Ask which sources the system may draw from, and whether unverified web content can be excluded.

Limitations and tradeoffs

Tradeoff: grounding constrains what the assistant can say, so answers become narrower and sometimes less fluent, and it depends on your source data being complete and current. Bad inputs still produce grounded but wrong answers.

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Connect this decision to your automation, organic marketing and customer lifecycle management. In a MeshLine demo, discuss your existing tools, the scope you need and how to measure the result.

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