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Understanding Results
Learn how to interpret optimization results and apply improvements.
Results Overview
When an optimization completes, you'll see:
- Winner: The best-performing variant (or original if none improved)
- Lift: Percentage improvement over the original
- Confidence: How reliable the result is
- Metrics: Detailed performance breakdown
Reading the Results
Winner Status
| Status | Meaning |
|---|---|
| Variant Won | A variant outperformed the original |
| Original Won | Your prompt is already optimal |
| Inconclusive | Not enough difference to declare a winner |
Lift Percentage
The improvement compared to your original prompt:
- +20% task completion = Users complete their goals 20% more often
- +15% sentiment = Users feel 15% more positive about interactions
- -10% response length = Responses are 10% more concise
Confidence Level
How sure we are about the result:
| Confidence | Meaning |
|---|---|
| High (>95%) | Very reliable, safe to deploy |
| Medium (80-95%) | Likely accurate, consider more testing |
| Low (<80%) | Inconclusive, run more simulations |
Viewing Variant Details
Dashboard
Click on any variant to see:
- Full prompt content
- Side-by-side comparison with original
- Sample conversations
- Metric breakdown
Via MCP
Show me the variants from my last optimizationCompare variant B to my original promptVia SDK
typescript
const variants = await converra.optimizations.getVariants('opt_123');
variants.forEach(v => {
console.log(`${v.name}:`);
console.log(` Task completion: ${v.metrics.taskCompletion}%`);
console.log(` Lift: ${v.metrics.lift}%`);
});Metrics Explained
Task Completion
Did the AI help users achieve their goal?
- High: Users got what they needed
- Low: Users left without resolution
Response Quality
Was the response accurate, helpful, and appropriate?
- High: Clear, correct, actionable responses
- Low: Vague, incorrect, or unhelpful
User Sentiment
How would users feel about the interaction?
- Positive: Satisfied, happy
- Neutral: Neither satisfied nor dissatisfied
- Negative: Frustrated, disappointed
Conciseness
Are responses appropriately sized?
- Good: Right length for the context
- Too long: Verbose, could be shortened
- Too short: Missing important information
Applying the Winner
When you're ready to use the winning variant:
Dashboard
Click Apply Winner on the results page.
Via MCP
Apply the winning variant from my last optimizationVia SDK
typescript
await converra.optimizations.applyVariant('opt_123');
// The winning variant is now your prompt's contentWhat Happens
- Your prompt content is updated to the winning variant
- A new version is created in version history
- Your original is preserved (can revert anytime)
- Cache is invalidated (SDK users get new content)
When No Clear Winner
If results are inconclusive:
- Run validation mode - More simulations = clearer results
- Adjust intent - Focus on specific improvements
- Review manually - Sometimes human judgment is needed
- Keep original - If it's working, don't change it
Learning from Results
What Worked
Look at winning variant changes:
- Added examples? → Examples help.
- Restructured? → Format matters.
- Changed tone? → Audience preference revealed.
What Didn't Work
Failed variants show what to avoid:
- Too formal? Too casual?
- Too verbose? Too terse?
- Missing context? Over-explained?
Sample Results
Optimization Complete: opt_abc123
Winner: Variant B (+23% lift)
Confidence: High (97%)
Metrics vs Original:
┌──────────────────┬──────────┬───────────┬────────┐
│ Metric │ Original │ Variant B │ Change │
├──────────────────┼──────────┼───────────┼────────┤
│ Task Completion │ 72% │ 89% │ +17% │
│ Response Quality │ 81% │ 94% │ +13% │
│ User Sentiment │ 68% │ 85% │ +17% │
│ Conciseness │ 65% │ 78% │ +13% │
└──────────────────┴──────────┴───────────┴────────┘
Key Changes in Variant B:
- Added step-by-step format for instructions
- Included acknowledgment before solutions
- Added follow-up confirmation questionNext Steps
- Logging Conversations - Track real performance
- Analyzing Insights - Understand patterns
