LLM Evaluation Hub
Ship changes without regressions: eval suite, CI gates, monitoring
This hub helps you set up a measurement system: test harness, benchmarks, CI gates, and monitoring signals so quality doesn't drift over time. Eval strategy, judge model pitfalls, and before/after proof.
Common eval & regression pains
Offline vs online evaluation
Both matter. Offline for fast gates; online for real-world correlation and drift detection.
Offline evaluation
Run on fixed test set before release. Fast feedback; no production traffic. Use for pre-merge and pre-release gates.
Online evaluation
Measure on live traffic (sampled). Reflects real user distribution and edge cases. Lag between deploy and signal.
Hybrid
Offline gates for blocking regressions; online for drift detection and correlation with business outcomes.
From logs, sampling, labeling
How to build an LLM eval suite that correlates with business KPI. Start from production evidence.
From production logs
Sample failures and successes. Stratify by cohort, use case, query type. Golden set that reflects real traffic.
Sampling strategy
Don't just take random. Oversample edge cases, failures, high-value queries. Balance coverage vs size.
Labeling
Human labels for ground truth. Rubrics for consistency. Consider LLM-as-judge with human calibration.
Versioning
Eval set evolves. Version it. Track which model/config was validated against which set.
Accuracy, consistency, groundedness, safety
Quality metrics for LLM apps. Pick metrics that matter for your use case and correlate with outcomes.
Accuracy / task success
Did the model do the right thing? Task-specific: classification, extraction, generation quality.
Consistency
Same query, multiple runs: do answers agree? Reduces 'random' feel; catches non-determinism.
Groundedness
Is the answer supported by context? Hallucination detection. Citation coverage. Ungrounded rate.
Safety / policy
Refusal correctness, PII leakage, policy violations. Safety eval suite separate from quality.
CI gates + release checklist
Prevent LLM regressions in production. Gate at merge, gate at release, monitor post-deploy.
Pre-merge: smoke eval
Small, fast suite on every PR. Catches obvious breakage. Must complete in minutes.
Pre-release: full eval
Larger suite before deploy. Block release if metrics drop below threshold. Versioned thresholds.
Release checklist
Manual sign-off for high-risk changes. Eval results attached. Rollback plan documented.
Post-release: canary + monitoring
Compare canary vs baseline. Alert on drift. Auto-rollback if critical metrics fail.
Scorecards, drift & regression detection
Dashboards, alerts, and correlation with business outcomes. Don't let quality drift silently.
Scorecards
Dashboard: accuracy, groundedness, latency, cost by cohort. Track over time. Set SLOs.
Drift detection
Input distribution, output distribution, metric trends. Alert when drift exceeds threshold.
Regression detection
Compare current vs baseline. Statistical significance. Don't alert on noise.
Correlation with business KPI
Link eval metrics to conversion, support tickets, user satisfaction. Prove eval matters.
Methodology: prove improvement
Define baseline, run A/B or shadow, document results. Avoid eval flakiness from judge model pitfalls.
Define baseline
Establish current metrics before change. No baseline = no proof of improvement.
Run A/B or shadow
Compare new vs old on same traffic. Statistical rigor: sample size, confidence intervals.
Document and version
Before/after numbers in release notes. Eval set version. Reproducible.
Avoid judge model pitfalls
Eval flakiness from judge model: temperature, prompt drift, calibration. Use multiple judges or human spot-checks.
Judge model pitfalls
Eval flakiness often comes from judge model: temperature, prompt drift, calibration. Use multiple judges, human spot-checks, or deterministic rubrics where possible.
Practical eval guides
Step-by-step playbooks to build eval suites, regression gates, and CI integration.
Evaluation should end in release confidence you can prove
The hub already explains the method. These case studies add the missing proof layer: benchmark evidence, concrete artifacts, and governance outcomes after the eval system went live.
Need an eval suite or regression gates?
We help teams build LLM eval frameworks, CI gates, and monitoring so you can ship changes without regressions. For ongoing governance and drift control, the Reliability Retainer — regression gates + monitoring operationalizes this week-to-week.
Start → Fix → Govern
Enforce the Audit → Sprint → Retainer ladder
Enterprise outcomes require a baseline, shipped fixes, then governance. This is the shortest path to measurable quality, controlled cost, and regression prevention.