TL;DR

A/B testing compares two AI variations (different prompts, models, or parameters) to see which performs better. Measure business metrics, not just AI metrics.

Why A/B test AI?

Validate changes:

  • Does new prompt improve results?
  • Is GPT-4 worth the cost vs GPT-3.5?
  • Does higher temperature increase engagement?

Measure real impact:

  • User satisfaction
  • Conversion rates
  • Time saved

What to test

Prompts:

  • Different instructions
  • Few-shot vs zero-shot
  • System message variations

Models:

  • GPT-4 vs GPT-3.5
  • Claude vs ChatGPT
  • Fine-tuned vs base

Parameters:

Features:

  • RAG vs no RAG
  • With/without examples
  • Different retrieval strategies

Setting up an A/B test

1. Define hypothesis:

  • "Adding examples to prompts will increase accuracy by 10%"

2. Choose metric:

  • User rating
  • Task completion rate
  • Click-through rate

3. Split traffic:

  • 50/50 for most tests
  • 90/10 for risky changes

4. Determine sample size:

  • Use statistical power calculator
  • Typically need hundreds-thousands of samples

5. Run test:

  • Maintain consistency
  • Watch for external factors (holidays, etc.)

6. Analyze results:

  • Statistical significance (p < 0.05)
  • Practical significance (is improvement meaningful?)

Metrics to track

AI performance:

User behavior:

  • Engagement (clicks, time spent)
  • Satisfaction (ratings, feedback)
  • Retention

Business outcomes:

  • Conversion rate
  • Revenue
  • Support cost reduction

Common pitfalls

Not running long enough:

  • Need sufficient sample size
  • Account for weekly patterns

Multiple comparisons:

  • Testing many variations inflates false positives
  • Use corrections (Bonferroni, etc.)

Ignoring segments:

  • Change might help some users, hurt others
  • Analyze by segment

Focusing on wrong metrics:

  • Optimizing for AI metrics that don't impact business
  • Always tie to user/business outcomes

Sequential testing

Test one change at a time:

  • Easier to identify what caused improvement
  • Compound changes make attribution hard

Multi-armed bandits:

  • Allocate more traffic to winning variant automatically
  • Faster convergence
  • More complex to implement

Tools and platforms

  • Optimizely, LaunchDarkly (feature flags + A/B)
  • Custom analytics dashboards
  • Statistical libraries (Python: scipy, statsmodels)

Interpreting results

Statistically significant + meaningful:

  • Roll out to all users

Statistically significant + tiny improvement:

  • Consider cost/complexity trade-off

Not significant:

  • Keep control variant
  • Or run longer

Negative result:

  • Don't deploy
  • Learn and iterate

What&#39;s next

  • Monitoring AI Systems
  • Evaluation Metrics
  • Prompt Engineering