A/B Testing for LLMs
Learn how to run controlled experiments to improve your LLM applications in production
Why A/B Test LLMs?
A/B testing allows you to validate changes to your LLM application with real users before full deployment. This is critical because:
- • LLM outputs are non-deterministic and context-dependent
- • User preferences may differ from internal evaluations
- • Production data reveals edge cases not in test sets
- • Statistical significance reduces risk of bad deployments
Setting Up an Experiment
Define Your Variants
Start by creating two or more variants of your LLM configuration:
const variants = {
control: {
model: "gpt-4",
temperature: 0.7,
prompt: "Answer concisely..."
},
treatment: {
model: "gpt-4",
temperature: 0.3,
prompt: "Provide a detailed..."
}
}Choose Success Metrics
Define what success looks like for your experiment:
- • User engagement: Click-through rates, time spent
- • Quality metrics: Thumbs up/down, user ratings
- • Task completion: Conversion rates, success rates
- • Performance: Latency, token usage
Running the Experiment
Use our platform to randomly assign users to variants and track results:
// Initialize experiment
const experiment = await platform.createExperiment({
name: "prompt-optimization",
variants: ["control", "treatment"],
trafficSplit: [0.5, 0.5]
})
// Get variant for user
const variant = experiment.getVariant(userId)
// Track outcome
await experiment.track(userId, {
variant,
metric: "user_satisfaction",
value: 4.5
})Analyzing Results
Statistical Significance
Wait for statistical significance before making decisions. Our platform calculates p-values and confidence intervals automatically. Generally, you need:
- • At least 100 samples per variant
- • p-value < 0.05 for 95% confidence
- • Consistent results over time
Best Practices
Test One Thing
Change only one variable at a time to understand what drives improvements
Run Long Enough
Collect data for at least one full business cycle to account for variations
Monitor Cost
Track token usage and API costs across variants to ensure improvements are cost-effective
Document Everything
Keep detailed records of hypotheses, configurations, and results for future reference