Evaluating LLMs for Enterprise: A Practical Benchmarking Framework

Why Generic Benchmarks Fail

MMLU, HumanEval, and other public benchmarks measure general capability - but enterprise applications are not general. A model that scores 90% on MMLU might fail catastrophically on your company's specific terminology, document formats, or compliance requirements. The correlation between public benchmark scores and real-world enterprise performance is surprisingly low.

Building Domain-Specific Evaluation Sets

Effective evaluation starts with building test sets that represent your actual production distribution. We collect real queries from the target use case (with PII removed), categorize them by difficulty and type, and create gold-standard answers verified by domain experts. A robust evaluation set typically needs 200–500 examples covering the full distribution of expected inputs.

• Query collection: real production queries, anonymized
• Stratified sampling: balanced across difficulty levels
• Gold standards: expert-verified reference answers
• Edge cases: deliberately adversarial and out-of-scope inputs
• Minimum 200 examples for statistically significant results

Multi-Dimensional Scoring

A single accuracy score hides critical failure modes. We evaluate LLMs across multiple dimensions simultaneously: factual correctness, format compliance, refusal behavior (does it say "I don't know" when it should?), hallucination rate, latency distribution, and cost per query. Each dimension gets its own metric, threshold, and monitoring.

• Correctness: semantic similarity + exact match + human review
• Format compliance: structured output validation
• Safety: refusal rate on out-of-scope queries
• Hallucination rate: claim verification against source documents
• Latency: P50, P90, P99 response times
• Cost: tokens consumed per query class

A/B Testing in Production

Lab evaluation is necessary but not sufficient. Production traffic reveals failure modes that no test set can anticipate. We implement shadow deployments where a candidate model processes real traffic in parallel with the production model, with outputs compared automatically. This provides high-confidence performance data before any user-facing switchover.

Continuous Evaluation Pipelines

LLM evaluation is not a one-time event. Model providers update their APIs, user query distributions shift, and enterprise data changes over time. We build continuous evaluation pipelines that run the full test suite on a weekly cadence, alerting engineering teams when any metric degrades beyond acceptable thresholds. This catches regressions before users notice them.