Production Evaluation Pipelines
Build CI/CD evaluation pipelines for LLMs: regression detection, monitoring, and hybrid human-AI evaluation
What are Production Evaluation Pipelines?
Production evaluation pipelines are automated systems that continuously assess LLM quality throughout the development lifecycle—from pre-commit checks to production monitoring. Unlike one-time benchmark runs, these pipelines provide continuous feedback on model quality.
Why it matters: 45% of organizations now have evaluation integrated into CI/CD. Without automated evaluation, you risk deploying regressions, missing quality degradation, and spending excessive time on manual review.
Key components: Pre-commit hooks, PR-based evaluation, staging tests, production monitoring, regression detection, and alerting systems.
Evaluation Pipeline Architecture
Pipeline Stages
Development Time
- • Prompt template validation
- • Quick regression tests
- • Format compliance checks
Review Time
- • LLM-as-Judge evaluation
- • Benchmark comparisons
- • Human spot-checks
Production Time
- • Real-time monitoring
- • Regression alerts
- • User feedback analysis
CI/CD Integration Patterns
Pre-commit Checks
- • Prompt validation: Check template syntax
- • Quick smoke tests: Run 5-10 critical examples
- • Format checks: JSON schema validation
- • Cost estimation: Token count changes
PR Evaluation
- • Full test suite: Run all evaluation benchmarks
- • A/B comparison: Compare vs. main branch
- • LLM-as-Judge: Automated quality scoring
- • Regression report: Highlight degradations
Hybrid Human-AI Evaluation
The 50/50 Strategy
Best results come from combining LLM-as-Judge (breadth) with human review (depth). Research shows that hybrid approaches outperform either method alone.
LLM-as-Judge (Automated)
- ✓ Evaluate 100% of outputs
- ✓ Consistent scoring criteria
- ✓ Fast turnaround (minutes)
- ✓ Cost-effective at scale
- ⚠ Limited domain expertise
Human Review (Manual)
- ✓ Deep domain expertise
- ✓ Nuanced quality assessment
- ✓ Edge case detection
- ✓ User perspective
- ⚠ Limited coverage (5-10%)
When to Escalate to Human Review
- • LLM-as-Judge confidence below threshold (<0.7)
- • High-stakes outputs (customer-facing, legal, financial)
- • Novel input patterns not in training distribution
- • Significant regression detected vs. baseline
- • Random sampling for calibration (5% of all outputs)
Regression Detection
Regression Alert Triggers
- • Safety score drops >5%
- • Error rate increases >10%
- • Core metric drops >15%
- • Quality score drops >5%
- • Latency increases >20%
- • Cost per query up >15%
- • Minor metric fluctuations
- • New warning patterns
- • Usage pattern changes
Production Monitoring
Real-time Metrics
- • Response quality scores
- • Latency percentiles (p50, p95, p99)
- • Error rates by type
- • Token usage and costs
- • Throughput (requests/sec)
Quality Indicators
- • LLM-as-Judge scores
- • User satisfaction (thumbs)
- • Regeneration rate
- • Session completion rate
- • Human override frequency
Safety Monitoring
- • Content filter triggers
- • Refusal rate trends
- • Jailbreak attempt detection
- • PII exposure incidents
- • Policy violation flags
Alerting & Rollback Procedures
Automatic Rollback Triggers
- • Error rate exceeds 5% for 5 minutes
- • Safety score drops below threshold
- • P99 latency exceeds SLA by 2x
- • User satisfaction drops >20%
- • Cost per query spikes >50%
Rollback Best Practices
- • Keep previous version hot-standby
- • Use feature flags for gradual rollout
- • Implement canary deployments (5% → 25% → 100%)
- • Maintain rollback runbook
- • Test rollback procedure regularly
Tool Integration
| Tool | Best For | CI/CD Integration |
|---|---|---|
| Promptfoo | Prompt testing and comparison | GitHub Actions, GitLab CI |
| Deepchecks | ML testing in CI/CD | Native CI/CD plugins |
| Arize Phoenix | Production monitoring | SDK integration |
| LangSmith | LangChain ecosystem | Python SDK |
| Grafana + Prometheus | Custom metrics dashboards | Metrics exporters |
Implementation Checklist
Setup Phase
- 1Define quality metrics and thresholds
- 2Create baseline evaluation dataset
- 3Configure LLM-as-Judge prompts
- 4Set up CI/CD pipeline stages
Operations Phase
- 5Monitor regression alerts daily
- 6Review human evaluation samples weekly
- 7Update evaluation dataset monthly
- 8Recalibrate thresholds quarterly