A/B Testing and LLM Rollouts

A/B Testing for LLMs Unlike traditional software, LLM outputs are non-deterministic and subjective. A/B testing helps you make data-driven decisions about prompt changes, model upgrades, and parameter tuning.

Experiment Design

What to Test

Metrics to Compare 1. Task Metrics: Accuracy, success rate 2. User Metrics: Thumbs up/down, NPS 3. Performance: Latency, cost 4. Safety: Guardrail violations

Statistical Considerations

• Sample size: Need enough users/requests for significance
• Duration: Run long enough to capture variance
• Segmentation: Consider user segments separately
• Multiple hypothesis correction: Adjust p-values for multiple metrics

Canary Deployments

Rollout Strategy

[New Model Version]
     ↓
[Deploy to 1%] → Monitor → [Issues?] → Rollback
     ↓
[Increase to 10%] → Monitor
     ↓
[Increase to 50%] → Monitor
     ↓
[Full rollout at 100%]

Monitoring During Rollout

Feature Flags for LLMs

**Example using LaunchDarkly pattern**
if feature_flags.is_enabled("use_gpt4_turbo", user_id):
    model = "gpt-4-turbo"
else:
    model = "gpt-4"

Online Evaluation

LLM-as-Judge Use a capable LLM to evaluate outputs:

Rate the following response on helpfulness (1-5):
Question: {question}
Response: {response}
Rating:

Human Evaluation Sampling

• Sample 1-5% of requests for human review
• Use rating scales (1-5) for consistency
• Track inter-annotator agreement

Tools for Experimentation