LLM Production
LLM Evaluation in Production β Continuous Quality Assurance
Evaluating LLMs in production requires a combination of automated metrics, human feedback, and statistical analysis to ensure consistent quality at scale.
- Online Evaluation β Real-time quality assessment during serving
- User Feedback β thumbs up/down, ratings, and implicit signals
- Automated Judging β LLM-as-a-judge and reference-based evaluation
You can't improve what you can't measure.
LLM Evaluation in Production
Offline evaluation on held-out benchmarks is necessary but insufficient for production LLMs. Real-world performance depends on user behavior, data distribution shifts, and interactions that benchmarks cannot capture. This guide covers continuous evaluation strategies for production systems.
Evaluation Dimensions
Task-Level Metrics
| Metric | Type | Description |
|---|---|---|
| Accuracy | Objective | Correct answers for factual tasks |
| BLEU/ROUGE | Reference-based | n-gram overlap with references |
| Perplexity | Intrinsic | Language modeling quality |
| Win Rate | Comparative | Preference vs. baseline model |
| Pass@k | Code | At least one correct solution in k samples |
User Experience Metrics
| Metric | Collection Method | Description |
|---|---|---|
| Thumbs Up/Down | Explicit | User satisfaction signal |
| Copy Rate | Implicit | User copied the response |
| Regenerate Rate | Implicit | User requested a new response |
| Session Length | Implicit | Engagement indicator |
| Task Completion | Implicit | User achieved their goal |
LLM-as-a-Judge
Bias Mitigation
| Bias | Description | Mitigation |
|---|---|---|
| Position bias | Preferring first/last option | Randomize option order |
| Verbosity bias | Preferring longer responses | Control for length |
| Self-preference | Preferring own outputs | Use different model families |
| Authority bias | Preferring confident tone | Calibrate with human labels |
Feedback Collection
Explicit Feedback
Feedback Architecture:
Implicit Feedback
Statistical Quality Control
Control Charts
Alert Thresholds
| Signal | Warning | Critical | Action |
|---|---|---|---|
| Thumbs down rate | > 15% | > 25% | Investigate recent changes |
| Regenerate rate | > 20% | > 35% | Review prompt/model quality |
| Safety violations | > 0.1% | > 0.5% | Immediate investigation |
| Latency p99 | > 2x baseline | > 5x baseline | Check infrastructure |
Continuous Improvement Loop
Practice Exercises
-
Conceptual: Compare the strengths and weaknesses of explicit versus implicit feedback for LLM evaluation. Under what conditions is each more reliable?
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Mathematical: Calculate Cohen's kappa for a judge that agrees with human raters 85% of the time, given that human raters agree with each other 80% of the time.
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Practical: Design an automated quality assurance system that detects quality degradation within 1 hour of a model update, using a combination of automated metrics and user feedback signals.
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Research: Evaluate the validity of LLM-as-a-judge for assessing factual accuracy. What are the failure modes and how can they be mitigated?
What to Learn Next
-> LLM Fine-Tuning Pipelines End-to-end fine-tuning infrastructure and data management.
-> AB Testing for LLMs Experiment design, statistical significance, and canary deployments.
-> LLM Monitoring and Observability Logging, tracing, metrics, and drift detection for production systems.
-> LLM Versioning and Rollouts Model versioning, artifact management, and gradual rollout strategies.
-> LLM Disaster Recovery Failover, backup models, and graceful degradation strategies.
-> LLM Security Best Practices Protecting systems from adversarial attacks and data privacy risks.