Glossary

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Glossary / Evaluation and implementation guide

Retrieval-Augmented Generation (RAG)

Retrieval-augmented generation (RAG) retrieves relevant material from a document collection and supplies it to a language model as context for an answer.

It can help an assistant use information outside its training data, such as current product documentation. Retrieval quality, source freshness, access permissions and the model’s use of that context all affect the result.

RAG does not eliminate hallucinations.

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A practical example

Example: a support agent using RAG answers a question about your refund policy by pulling the current policy page and quoting it, rather than reproducing a possibly outdated training-memory version.

What to evaluate before investing

  • Ask how documents are chunked and indexed, and whether you can control what counts as a source of truth.
  • Test with questions whose answers changed recently, to see whether stale content is still retrieved.
  • Check whether answers cite their sources visibly, so users and reviewers can verify claims.

Limitations and tradeoffs

RAG only grounds answers in what you index; gaps, duplicates, or contradictions in your knowledge base surface directly as bad 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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