A/B Testing

Optimize your prompts with sophisticated A/B testing and performance analytics

A/B Testing

A/B testing in PromptCompose allows you to experiment with different prompt variations to optimize performance, improve user engagement, and make data-driven decisions about your AI interactions.

What is A/B Testing?

A/B testing lets you compare multiple versions of a prompt to see which performs better. You can test:

  • Different wordings for the same request
  • Various approaches to solving a problem
  • Different personalization levels
  • Alternative call-to-action styles

The system automatically distributes traffic between variants and tracks performance metrics.

Key Concepts

Tests and Variants

  • Test: The overall experiment comparing different approaches
  • Variants: Different versions of the prompt being tested
  • Control: The original or baseline version (usually labeled “Control”)
  • Treatment: The new versions you’re testing against the control

Rollout Strategies

  • Weighted: Randomly distribute users based on percentages
  • Sequential: Test variants one after another in cycles
  • Manual: Explicitly control which variant each user sees

Success Metrics

  • Conversion Rate: How often the desired outcome occurs
  • Engagement: User interaction and response quality
  • Business Impact: Revenue, signups, or other business goals

Creating A/B Tests

Basic Test Setup

  1. Start from a Prompt

    • Go to an existing prompt you want to test
    • Click “Create A/B Test”

  2. Test Configuration

    • Test Name: Descriptive name (e.g., “Welcome Email - Tone Test”)
    • Description: What you’re testing and why
    • Hypothesis: What you expect to learn
    • Success Metric: How you’ll measure success

  3. Test Timeline

    • Start Date: When to begin the test
    • End Date: When to automatically stop
    • Minimum Duration: Ensure statistical significance

Variant Creation

Control Variant

The control is typically your current prompt:

Subject: Welcome to {company_name}!

Hello {customer_name},

Welcome to {company_name}. We're excited to have you as a customer.

Here's what you can expect:
- {feature_1}
- {feature_2}
- {feature_3}

If you have any questions, contact us at {support_email}.

Best regards,
{company_name} Team

Treatment Variants

Create variations to test different approaches:

Variant A: More Personal

Subject: {customer_name}, welcome to your {company_name} journey!

Hi {customer_name},

I'm personally excited to welcome you to {company_name}! 

As a new member, you now have access to:
→ {feature_1}
→ {feature_2} 
→ {feature_3}

Need help getting started? I'm here for you at {support_email}.

Cheers,
{agent_name}

Variant B: Benefit-Focused

Subject: Your {company_name} benefits are ready!

Dear {customer_name},

You're all set! Here are the immediate benefits waiting for you:

✓ {feature_1} - {benefit_1}
✓ {feature_2} - {benefit_2}
✓ {feature_3} - {benefit_3}

Start exploring: {getting_started_url}

Questions? We're here: {support_email}

Rollout Strategy Configuration

Weighted Distribution

Randomly assign users based on percentages:

  • Control: 50%
  • Variant A: 25%
  • Variant B: 25%

Best for:

  • Standard A/B testing
  • Even traffic distribution
  • Statistical significance testing

Sequential Testing

Test variants in rotating cycles:

  • Week 1: Control
  • Week 2: Variant A
  • Week 3: Variant B
  • Repeat…

Best for:

  • Seasonal effects consideration
  • Gradual rollouts
  • Time-based comparisons

Manual Assignment

Explicitly control which users see which variant:

  • Premium customers → Variant A
  • New users → Variant B
  • Default users → Control

Best for:

  • Targeted testing
  • User segment analysis
  • Feature flags and rollouts

Managing Active Tests

Test Dashboard

Monitor your tests from the A/B Testing dashboard:

Test Status Overview

  • Active: Currently running tests
  • Paused: Temporarily stopped tests
  • Completed: Finished tests with results
  • Scheduled: Tests waiting to start

Performance Metrics

  • Impressions: How many times each variant was shown
  • Conversions: Successful outcomes per variant
  • Conversion Rate: Success percentage by variant
  • Statistical Significance: Confidence in results

Real-Time Monitoring

Track test performance as it happens:

Traffic Distribution

  • Verify users are being assigned to variants correctly
  • Check for even distribution (weighted tests)
  • Monitor user session consistency

Early Indicators

  • Conversion trends by variant
  • User engagement patterns
  • Error rates or issues

Performance Alerts

  • Significant performance differences
  • Statistical significance reached
  • Technical issues or errors

Test Controls

Manage tests while they’re running:

Pause/Resume

  • Stop tests temporarily without losing data
  • Resume when issues are resolved
  • Useful for fixing problems or seasonal breaks

Traffic Adjustment

  • Change variant percentages mid-test
  • Redirect more traffic to winning variants
  • Respond to early performance indicators

Emergency Stop

  • Immediately stop underperforming tests
  • Protect users from poor experiences
  • Switch all traffic to best variant

Analyzing Results

Performance Metrics

Conversion Tracking

Set up conversion events that matter to your business:

  • Email Opens: For email subject line tests
  • Click-Through: For call-to-action tests
  • Sign-Ups: For onboarding flow tests
  • Purchases: For sales prompt tests
  • Support Resolution: For customer service tests

Statistical Analysis

Understand the confidence in your results:

  • Sample Size: How many users saw each variant
  • Confidence Level: Usually 95% or 99%
  • P-Value: Statistical significance indicator
  • Margin of Error: Range of uncertainty

Business Impact

Translate results into business terms:

  • Revenue Impact: Dollar value of improvements
  • User Experience: Satisfaction and engagement
  • Efficiency Gains: Time saved or process improvements
  • Risk Reduction: Fewer errors or complaints

Test Results Interpretation

Clear Winner

When one variant significantly outperforms others:

Control: 12.3% conversion rate (1,000 users)
Variant A: 18.7% conversion rate (1,000 users) ✓ Winner
Variant B: 11.8% conversion rate (1,000 users)

Statistical Significance: 99%
Improvement: +52% over control

Action: Deploy the winning variant

No Significant Difference

When variants perform similarly:

Control: 15.2% conversion rate (2,000 users)
Variant A: 15.8% conversion rate (2,000 users)
Variant B: 14.9% conversion rate (2,000 users)

Statistical Significance: Not reached
Difference: Within margin of error

Action: Continue testing or try more different approaches

Inconclusive Results

When you need more data:

  • Extend test duration
  • Increase traffic allocation
  • Simplify the test (fewer variants)
  • Check for external factors

Segment Analysis

Analyze results by user segments:

Demographics

  • Age groups
  • Geographic regions
  • Device types

Behavior

  • New vs. returning users
  • Engagement levels
  • Purchase history

Context

  • Time of day
  • Day of week
  • Seasonal effects

Advanced Testing Strategies

Multi-Variate Testing

Test multiple elements simultaneously:

Variables to test:
- Subject line: [Personal, Professional, Benefit-focused]
- Greeting: [Hi, Hello, Dear]
- Call-to-action: [Get Started, Learn More, Try Now]

Total combinations: 3 × 3 × 3 = 27 variants

Use when:

  • You want to test interactions between elements
  • You have high traffic volume
  • You want to optimize multiple components

Sequential Testing

Run tests in phases:

  1. Phase 1: Test broad concepts (tone, approach)
  2. Phase 2: Test specific wording within winning concept
  3. Phase 3: Test final details (buttons, formatting)

Holdout Testing

Keep a control group throughout multiple test cycles:

  • Holdout: Always gets original version
  • Test Group: Gets current winning version
  • Long-term Impact: Measure cumulative effect

Best Practices

Test Design

  1. Clear Hypothesis: Know what you’re testing and why
  2. One Variable: Test one major change at a time
  3. Significant Difference: Make changes big enough to matter
  4. Realistic Timeline: Allow enough time for statistical significance
  5. Business Impact: Focus on metrics that matter to your business

Statistical Rigor

  1. Sample Size: Calculate required users before starting
  2. Test Duration: Run long enough for significance
  3. External Factors: Account for seasonality and events
  4. Multiple Testing: Adjust for testing multiple variants
  5. Stopping Rules: Decide when to stop before starting

User Experience

  1. Consistency: Users should see the same variant throughout their session
  2. Fairness: Don’t disadvantage any user group
  3. Quality: All variants should meet quality standards
  4. Fallback: Have backup plans for technical issues

Organizational Alignment

  1. Stakeholder Buy-in: Get agreement on test goals
  2. Success Criteria: Define winning conditions upfront
  3. Decision Making: Plan how you’ll act on results
  4. Documentation: Record learnings for future tests

Common Testing Scenarios

Email Optimization

  • Subject line testing
  • Greeting personalization
  • Call-to-action placement
  • Content length variation

Customer Support

  • Response tone testing
  • Solution approach comparison
  • Escalation trigger optimization
  • Resolution time improvement

Onboarding Flows

  • Welcome message testing
  • Feature introduction order
  • Tutorial approach comparison
  • Activation prompt optimization

Sales and Marketing

  • Value proposition testing
  • Objection handling approaches
  • Closing technique comparison
  • Follow-up timing optimization

Integration with Development

API-Driven Testing

Tests work seamlessly with your applications through the SDK:

// SDK automatically handles A/B test assignment
const result = await promptCompose.resolvePrompt('welcome-email', {
  config: {
    abTesting: {
      sessionId: `user-${userId}` // Consistent experience
    }
  }
}, variables);

// Track conversion when user completes desired action
if (userSignedUp) {
  await promptCompose.reportABResult(result.abTest.publicId, {
    variantId: result.variant.publicId,
    status: 'success',
    sessionId: `user-${userId}`
  });
}

Conversion Tracking

Set up conversion events in your application:

  • Page Views: Track engagement
  • Button Clicks: Measure interaction
  • Form Submissions: Monitor completions
  • Purchases: Track revenue impact

Troubleshooting A/B Tests

Common Issues

Uneven Traffic Distribution

  • Check rollout strategy configuration
  • Verify user session handling
  • Review traffic allocation percentages

Low Statistical Significance

  • Increase test duration
  • Raise traffic allocation
  • Reduce number of variants

Conflicting Results

  • Check for external factors
  • Review user segment consistency
  • Verify conversion tracking setup

Technical Problems

  • Monitor error rates by variant
  • Check API integration health
  • Verify conversion event tracking

Getting Help

For A/B testing support:

  • Review test configuration settings
  • Check statistical significance calculations
  • Consult testing best practices guides
  • Contact support for complex issues

Next Steps

With A/B testing set up:

A/B testing is essential for optimizing your AI interactions - use it to continuously improve performance and deliver better user experiences.