1. Build Flow
   ↓ Create workflow with prompts
   ↓ Deploy for production use
   
2. Collect Results
   ↓ Flow processes data
   ↓ Saves outputs to dataset
   
3. Evaluate Quality
   ↓ Run evaluators on outputs
   ↓ Measure success metrics
   
4. Analyze & Improve
   ↓ Identify failure patterns
   ↓ Refine prompts in Playground
   ↓ Update flow with better prompts
   
5. Repeat
   ↓ Continuous monitoring
   ↓ Ongoing optimization

Flow with Dataset Sink:
  Prompt Node
    → out: AI-generated content
  Dataset Sink
    → Save: input, output, metadata
    
Dataset contains:
  - input: Original data
  - output: AI response
  - timestamp: When processed
  - flow_version: Which flow version

Evaluator: "Summary Quality"
  Input: output (from flow)
  Criteria:
    - Is factually accurate
    - Captures key points
    - Appropriate length
    - Professional tone
  Score: 1-5
  
Evaluator: "Response Relevance"
  Prompt: "Does this response address the input?"
  Input: input, output
  Score: Pass/Fail

1. Select dataset with flow outputs
2. Choose evaluators to run
3. Execute evaluation
4. Review scores and feedback

Flow processes 100 items
  → Saves to "Flow Outputs" dataset
  
Periodic evaluation:
  → Run evaluators on "Flow Outputs"
  → Generate quality report
  → Identify low-scoring items
  → Analyze failure patterns

Prompt 1: Generate content
  ↓
Prompt 2: Evaluate quality
  → "Rate this content 1-5"
  ↓
Conditional: If score < 3
  → Regenerate with different approach
  ↓
Dataset Sink: Save only high-quality outputs

For high-volume flows:
  Save: Every output
  Evaluate: Random 10% sample
  
Sufficient for:
  - Monitoring trends
  - Detecting quality issues
  - Lower evaluation costs

Evaluation shows:
  - 15% of outputs score < 3/5
  - Common issue: Missing key details
  - Pattern: Short input texts
  
Root cause:
  Prompt doesn't handle brief inputs well

1. Export low-scoring examples
2. Load into Playground
3. Test prompt variations
4. Run experiments with evaluators
5. Find better prompt
6. Measure improvement

1. Update prompt in Flow
2. Test with sample data
3. Deploy new version
4. Monitor quality metrics
5. Verify improvement

Run two flow versions:
  Version A: Original prompt (50% traffic)
  Version B: New prompt (50% traffic)
  
Compare results:
  Version A: 3.8/5 average
  Version B: 4.3/5 average ✓
  
Roll out: Deploy Version B to 100%

Prompt 1: "Generate product description"
  out: description
  ↓
Prompt 2: "Rate this description 1-5"
  input: description
  out: score, feedback
  ↓
If score >= 4:
  → Dataset Sink (approved)
If score < 4:
  → Dataset Sink (needs review)

Stage 1: Generate content
Stage 2: Check grammar/spelling
Stage 3: Verify factual accuracy
Stage 4: Assess tone/style
  
Only outputs passing all gates are saved

Flow outputs
  → Auto-evaluate
  → If score < threshold:
      → Flag for human review
      → Save to review queue
  → If score >= threshold:
      → Auto-approve
      → Save to production dataset

Option 1: Highest Quality
  Model: GPT-4
  Temperature: 0.3
  Evaluation: Comprehensive
  Cost: $1.50 per run
  Quality: 4.5/5
  
Option 2: Balanced
  Model: GPT-4
  Temperature: 0.7
  Evaluation: Key metrics only
  Cost: $0.75 per run
  Quality: 4.2/5
  
Option 3: Cost-Optimized
  Model: GPT-3.5
  Temperature: 0.7
  Evaluation: Sampling
  Cost: $0.15 per run
  Quality: 3.8/5

Flow: Ticket Response Generator
  Input: Customer question
  Output: Suggested response
  
Initial Performance:
  Quality: 3.5/5
  Approval Rate: 60%
  Issue: Too generic, misses details
  
Improvement Cycle:
  Week 1: Add context to prompt
    → Quality: 3.9/5
  Week 2: Use examples in prompt
    → Quality: 4.2/5
  Week 3: Add validation step
    → Quality: 4.4/5
  Week 4: Refine edge cases
    → Quality: 4.5/5
  
Final Result:
  Approval Rate: 85%
  Response Time: 2s (was 2 hours)
  Cost: $0.08 per ticket
  Customer Satisfaction: +15%

Set up alerts:
  - Quality drops below 4.0
  - Pass rate < 80%
  - Error rate > 5%
  - Cost increase > 20%

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