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You Can’t Improve What You Don’t Measure

Many teams iterate on prompts by “vibes” - does the output look good? - or by fixing one scenario at a time, and then don’t check for regression testing. That doesn’t scale. The Production Process: 
1. Define success criteria
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2. Create evaluation dataset
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3. Test current prompt
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4. Analyze failures
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5. Modify prompt
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6. Re-test → repeat
Example: Customer Sentiment Classification: AI Evaluation Tools: Several tools can help you evaluate your prompts:
  • Open Source: LangFuse, Inspect AI, Phoenix, Opik,
  • Commercial: Braintrust, Langsmith, Arize, AgentOps

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A/B Testing Prompts

Production Pattern: Gradual Rollout: Don’t deploy a new prompt to 100% of users immediately. Metrics to Track:
  • Task success rate
  • User satisfaction (thumbs up/down)
  • Response time
  • Cost per request
  • Error rate
Analysis After 1000 Requests: 
const results = {
    v2_prompt: {
        success_rate: 0.87,
        avg_latency: 1.2,
        cost_per_request: 0.05,
        satisfaction: 0.82
    },
    v3_prompt: {
        success_rate: 0.93,  // Better!
        avg_latency: 1.4,    // Slightly slower
        cost_per_request: 0.07,  // Slightly more expensive
        satisfaction: 0.89   // Much better!
    }
};

// Decision: v3 wins → roll out to 50%, then 100%

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Common Failure Patterns

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Pattern 1: Prompt Injection

Example:

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Pattern 2: Context Stuffing

Example:

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Pattern 3: Ambiguous Output Parsing

Example: Another alternative is to use a structured output format link