The first demo usually gets the flattering question. Production gets the rest. Evaluation is how you discover that difference before your users do, preferably without an incident named after a weekday.
Define success in the user’s terms
A document assistant succeeds when it returns a supported answer for the right user. A routing classifier succeeds when it sends a case to the right queue. A writing assistant succeeds when its draft is accurate and usable. “Sounds intelligent” is not an acceptance criterion.
List the failure costs. Missing an urgent support case may be worse than flagging an extra one. A wrong financial figure can be worse than an incomplete summary. This helps decide thresholds, review requirements, and what the system should refuse to do.
Build a compact test set
Start with realistic common tasks, then add important edge cases. Include ambiguous inputs, missing evidence, conflicting sources, and malicious content. Preserve expected outcomes and the reasoning behind them. If an answer can vary legitimately, define a rubric rather than one exact sentence.
Hold out cases you do not use while tuning the prompt. Otherwise you are teaching to your own tiny exam. Repeatedly adjusting against the same questions can make a system look more robust than it is.
Measure the stages
For RAG, test retrieval and answer support separately. For agents, test actions and permissions as well as final output. For multimodal systems, test perception before interpretation.
Record latency, cost, correction time, and failure category. An accurate answer after fifteen retries may be a poor user experience. A fast answer that sends the wrong file is not a speed victory.
Automated judges need calibration
A model can help score responses, but its judgments can depend on presentation, position, and the rubric. Compare automated judgments with human review on a representative subset. Investigate disagreements and severe errors rather than treating a single numeric score as ground truth.
Some checks are deterministic: does the output parse, does a referenced source exist, do totals add up, is the action authorized? Use those checks directly when appropriate. They are cheaper than asking a language model whether a missing file has a persuasive personality.
Benchmark claims need conditions
A benchmark result describes a particular test under particular conditions. Tools, retries, prompts, sampling, and contamination controls matter. A result is useful evidence, but it does not directly predict performance on your workflow.
The Stanford 2026 AI Index is a broad reference for interpreting industry trends across multiple dimensions. It is a starting point for perspective rather than a substitute for local testing.
Keep a regression habit
Re-run the same meaningful cases after changing models, prompts, retrieval, or tool access. Version the configuration. Save representative failures and make them future checks. Let the test set grow from actual usage, with private data removed or controlled.
Evaluation cannot prove a system will never fail. It can expose recurring problems, quantify tradeoffs, and make a deployment decision defensible. That is already much better than trusting the demo’s excellent lighting.
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