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Definition · AI in finance

AI evaluation

AI evaluation is systematically testing and scoring model or system outputs against benchmarks or ground truth. For AI evaluation, the useful boundary is the data it uses, the tools it can call, the approvals it needs, the review standard, and the finance decision it may influence before the output is trusted or automated.

Also known as AI evals, LLM evaluation, model evaluation

Written by Pluvo TeamReviewed by Pluvo Team
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Why it matters

Understanding AI evaluation matters because AI-assisted finance work can sound confident even when data, assumptions, or compute paths are wrong. A useful definition keeps the output grounded, reviewable, and accountable. Pluvo's figures are checkable against source records by construction, so evaluating an answer means tracing the number, not second-guessing a model's confidence.

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In practice

  • Governance example

    Teams use AI evaluation when they evaluate whether an AI-assisted analysis can be trusted. The useful test is whether the output is tied to approved data, repeatable logic, human review, and an audit trail.

  • Pluvo example

    Pluvo's figures are checkable against source records by construction, so evaluating an answer means tracing the number, not second-guessing a model's confidence.

In practice, teams should define AI evaluation with a clear source, owner, time period, and decision before they use it in reporting, planning, or operating reviews.

Understanding AI evaluation matters because AI-assisted finance work can sound confident even when data, assumptions, or compute paths are wrong. A useful definition keeps the output grounded, reviewable, and accountable. Pluvo's figures are checkable against source records by construction, so evaluating an answer means tracing the number, not second-guessing a model's confidence.

A strong workflow for AI evaluation separates the definition from the action: first agree what the term means, then decide how it is measured, when it changes, and who is accountable for the next step.

Pluvo's figures are checkable against source records by construction, so evaluating an answer means tracing the number, not second-guessing a model's confidence.

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FAQ

What is AI evaluation?

AI evaluation is systematically testing and scoring model or system outputs against benchmarks or ground truth. For AI evaluation, the useful boundary is the data it uses, the tools it can call, the approvals it needs, the review standard, and the finance decision it may influence before the output is trusted or automated.

How do you evaluate an LLM's output?

Measure AI evaluation against a defined standard: agreed source data, expected output, review threshold, and owner. That makes the answer testable instead of relying on whether the result merely looks plausible.

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Sources

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