Benchmarks are helpful, but they rarely predict business impact. In 2025, the highest‑performing teams evaluate LLM systems on how well they help users complete real tasks—accurately, safely, and quickly—while staying within budget.
Golden sets and human rubrics
Start with a curated set of real questions and workflows, including edge cases. Write a lightweight rubric that defines what a “good” answer looks like for usefulness, accuracy, safety, and tone. Human review on a small sample surfaces blind spots that automated metrics miss.
Automatic checks for hallucinations and grounding
Augment human review with automatic checks. For grounded systems (like RAG), verify that answers cite sources and quotes match the underlying text. Add detectors for unsupported claims, PII leakage, and unsafe content. These checks can run on every deployment.
Close the loop with user feedback
Put feedback where work happens: thumbs up/down, quick tags (helpful, wrong, off‑topic), and a free‑text note. Compare model and prompt variants via A/B tests and ship improvements only when they win on your golden set and live metrics.
Measure the KPIs that matter
Track task success rate, time to completion, deflection rate (for support), CSAT, error rate, latency, and cost. Trend them over time and break them down by use case and user segment. Report trade‑offs explicitly so stakeholders understand why a change shipped.
Make evaluation part of your development loop—not an afterthought—and your LLM products will steadily get more useful, reliable, and affordable.