Prompts Are Product
Every prompt you write is a product decision. The tone. The structure. The examples. The guardrails.
Yet most teams treat prompts like code comments—write once, hope for the best.
Your prompts deserve A/B testing.
The Framework
1. Define Your Metric
Before testing prompts, know what you’re optimizing:
- User satisfaction (thumbs up/down)
- Task completion rate
- Follow-up question frequency
- Time to value
2. Create Variants
Don’t test wildly different prompts. Test specific hypotheses:
Hypothesis: Adding examples improves output quality
Control: “Summarize this document.”
Variant: “Summarize this document. For example: ‘The Q3 report shows revenue increased 15% driven by enterprise sales.‘“
3. Split Traffic
Use Clayva’s experimentation:
clayva experiment create "prompt-with-examples" \
--metric "user_satisfaction" \
--rollout 50%4. Measure Everything
Track more than your primary metric:
- Latency (longer prompts = slower responses)
- Token usage (cost implications)
- Error rates
- User engagement post-response
5. Ship Winners Fast
With Clayva, you can see statistical significance in hours, not weeks. When a variant wins, ship it immediately.
Real Results
One team we worked with improved their AI assistant’s satisfaction score from 72% to 89% through systematic prompt testing. The winning change? Adding “Let me think step by step” to complex queries.
Small changes. Rigorous testing. Massive impact.
Start Today
Pick your worst-performing AI feature. Write a hypothesis. Create a variant. Test it this afternoon.
The prompt experimentation framework isn’t complex. It’s just disciplined.
