How do you evaluate an LLM before putting it in production?
To evaluate an LLM for production, score it against a representative evaluation set on the dimensions that matter for your use case: accuracy and faithfulness, hallucination and bias, safety and toxicity, robustness to adversarial input, plus latency and cost. Compare candidates and prompt versions on the same set, set thresholds, and gate releases so a regression cannot ship. Re-run as models and data change.
What makes a good LLM evaluation set?
A useful evaluation set mirrors real usage: representative prompts, edge cases, and known-hard examples, each with criteria for what 'good' looks like — correct, grounded, safe, on-tone. The set is the asset; it lets you compare models, prompts, and retrieval strategies objectively instead of by impression.
Combine automated scorers (exact match, similarity, faithfulness), model-based evaluation for nuanced judgment, and human review for the cases that need it. No single method is enough on its own.
How do you turn evaluation into a release gate?
Define thresholds for the metrics that matter and wire them into the pipeline so a model swap, prompt change, or data update runs the evaluation automatically and blocks the release if a key metric regresses. This is the AI equivalent of regression testing.
Because LLM behaviour drifts as providers update models and your data changes, evaluation is continuous, not a one-time sign-off. Track metrics over time so you catch silent degradation.
How Appsierra evaluates LLMs
Appsierra builds the evaluation sets, scorers, and pipeline gates that make LLM releases safe, with senior engineers owning the criteria and reviewing ambiguous results. Evaluation is the discipline we apply to our own AI products, which is why it anchors our positioning: AI in production, de-risked by real evaluation.
Our AI governance & evaluation and generative AI development services put a production-grade evaluation program in place.
Frequently asked questions
What metrics matter when evaluating an LLM?
Accuracy and faithfulness, hallucination and bias rates, safety and toxicity, robustness to adversarial input, and operational metrics like latency and cost. The exact weighting depends on your use case and risk profile.
How often should you re-evaluate an LLM in production?
Continuously. Providers update models, your data shifts, and prompts evolve — any of which can change behaviour. Run evaluation on every change and monitor key metrics over time to catch silent regressions.
Can you automate LLM evaluation?
Most of it, using automated scorers, model-based evaluation, and pipeline gates. Human review remains important for nuanced or high-stakes judgments, which is what keeps the automated scores honest.
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