AI Evaluations
AI evaluations are structured tests that measure whether an AI model or agent is performing correctly — catching errors, regressions, and edge cases before they reach production. Without evaluations, you are flying blind.
What are AI Evaluations?
An AI evaluation (often shortened to "evals") is a systematic process for testing AI outputs against a defined standard. You define what correct behaviour looks like — the right extraction, the right classification, the right decision — and then run your model against a set of test cases to measure how often it gets it right. Evaluations can be automated (checking outputs against expected answers) or human-reviewed (a subject matter expert scores the outputs).
This is not a one-time activity. Models degrade when input data shifts, when prompts change, or when external APIs update. A rigorous evaluation framework runs continuously and catches problems before they compound into operational errors.
What Good Evaluations Cover
A complete evaluation framework for an operational AI system typically includes:
Accuracy tests: Does the model extract the right fields from purchase orders? Does it classify exceptions into the correct categories?
Edge case tests: What happens with a malformed invoice? A supplier name with special characters? A currency not seen in training data?
Regression tests: After a prompt change or model update, do previously-correct outputs still pass?
Latency benchmarks: Does processing time stay within acceptable bounds at expected volumes?
AI Evaluations in Operations
For any AI agent handling operational data — invoice processing, order management, supplier communication — evaluations are a prerequisite for trust, not an optional extra. The finance controller who approves an automated three-way match process needs to know it has been tested against real exception scenarios. The supply chain lead deploying an order routing agent needs confidence it handles edge cases correctly. At Lleverage, evaluations are built into the deployment process for every agent: before go-live, against a held-out set of real operational documents. This is how you move from "it seems to work" to "we can rely on it."