Best Practices

Expert tips for effective model testing, evaluation, and deployment.

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Testing Strategy

1. Start Simple

✅ Begin with defaults:

• Temperature: 0.7
• Max Tokens: 500
• Single model (GPT-4o Mini)

Why: Establish a baseline before optimization.

2. Test Systematically

✅ Change one parameter at a time:

Test 1: Default settings
Test 2: Change temperature only
Test 3: Change max tokens only
Test 4: Combine best settings

❌ Don't: Change everything at once (can't identify what works).

3. Use Real Data

✅ Test with actual use case prompts:

• Real customer questions
• Actual data to analyze
• Production-like scenarios

✅ Leverage prompt history:

• Review what prompts worked before
• Re-run successful tests
• Track performance over time

❌ Don't: Only test with toy examples.

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Sample Size

Minimum Test Runs

Use Case	Min Tests	Why
Classification	50	Need statistical significance
Content Generation	10	Check consistency
Customer Support	20	Vary user intents
Data Analysis	15	Test edge cases

Testing Pattern

// ✅ Good: Multiple runs
for (let i = 0; i < 20; i++) {
  result = testModel(prompt, settings);
  results.push(result);
}
analyze(results);

// ❌ Poor: Single test
result = testModel(prompt, settings);
deployToProduction(); // Risky!

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Evaluation Criteria

Define Success Metrics

Before testing, define:

1. Quality Threshold

   Customer Support: 90% helpful responses
   Code Generation: 95% syntactically correct
   Data Classification: 98% accuracy

2. Performance Requirements

   Response Time: < 2 seconds
   Token Usage: < 500 tokens average
   Cost per Request: < $0.01

3. Consistency Standard

   Same input should yield similar quality
   Variation acceptable: ±10%

Measure Objectively

Quantitative Metrics:

• Accuracy (for classification)
• Response time (milliseconds)
• Token count (cost proxy)
• Word count (length)
• Completion rate

Qualitative Assessment:

• Relevance (1-5 scale)
• Clarity (1-5 scale)
• Professionalism (1-5 scale)
• Accuracy (1-5 scale)

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Model Selection

Decision Framework

1. Quality Requirements
   High (>95%) → GPT-4 Omni
   Medium (85-95%) → GPT-4o Mini
   Basic (70-85%) → GPT-3.5 Turbo

2. Volume Considerations
   Low (<1K/day) → Any model
   Medium (1K-10K/day) → GPT-4o Mini or GPT-3.5
   High (>10K/day) → GPT-3.5 Turbo + selective GPT-4

3. Budget Constraints
   Premium → GPT-4 Omni
   Standard → GPT-4o Mini
   Economy → GPT-3.5 Turbo

Hybrid Approach

Best Practice: Use multiple models

function selectModel(request) {
  // Complex reasoning → Premium model
  if (requiresDeepAnalysis(request)) {
    return "GPT-4-Omni";
  }
  
  // Standard questions → Mid-tier
  if (isStandardQuery(request)) {
    return "GPT-4o-Mini";
  }
  
  // Simple tasks → Economy
  return "GPT-3.5-Turbo";
}

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Parameter Optimization

Temperature Testing

Test at 3 points:

Low: 0.3
Medium: 0.7 (default)
High: 1.2

Choose based on:

• Consistency needed? → Use 0.3
• Balanced? → Use 0.7
• Creativity needed? → Use 1.2

Token Limit Optimization

Find the sweet spot:

// Test different limits
tokenLimits = [200, 400, 600, 800, 1000];

for (limit of tokenLimits) {
  result = test({maxTokens: limit});
  if (result.complete && result.cost < budget) {
    optimalLimit = limit;
    break;
  }
}

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Cost Management

Monitor Token Usage

Track these metrics:

metrics = {
  avgInputTokens: 150,
  avgOutputTokens: 300,
  totalTokens: 450,
  costPerRequest: 0.008,
  requestsPerDay: 1000,
  dailyCost: 8.00
}

Optimization Strategies

1. Prompt Optimization

   ❌ Verbose: "I would like you to please help me by..."
   ✅ Concise: "Summarize this text:"
   
   Savings: 20-30% fewer input tokens

2. Token Limits

   ❌ No limit: Average 800 tokens
   ✅ Set limit: 400 tokens
   
   Savings: 50% on output costs

3. Model Routing

   ❌ All GPT-4: $300/month
   ✅ Mixed models: $80/month
   
   Savings: 73%

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Quality Assurance

Pre-Deployment Checklist

• Tested with 20+ real prompts
• Success rate > threshold
• Response time acceptable
• Cost within budget
• Edge cases handled
• Error handling tested
• Monitoring in place

Continuous Testing

After deployment:

// Monitor production
setInterval(() => {
  metrics = collectMetrics();
  
  if (metrics.quality < threshold) {
    alert("Quality degradation detected");
  }
  
  if (metrics.cost > budget) {
    alert("Budget exceeded");
  }
}, 3600000); // Check hourly

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Common Mistakes

❌ Mistake 1: Testing Only Once

Problem: Single test doesn't show consistency

Solution: Run minimum 10 tests per configuration

❌ Mistake 2: Using Wrong Model

Problem: GPT-4 for simple classification (expensive)

Solution: Match model capability to task complexity

❌ Mistake 3: Ignoring Edge Cases

Problem: Works for normal inputs, fails on unusual ones

Solution: Test with:

• Empty inputs
• Very long inputs
• Special characters
• Multiple languages
• Ambiguous requests

❌ Mistake 4: No Cost Analysis

Problem: Production costs explode unexpectedly

Solution: Calculate costs before scaling:

Cost per Request × Requests per Day × 30 = Monthly Cost

❌ Mistake 5: Skipping Documentation

Problem: Can't reproduce or explain results

Solution: Document:

• Test configurations
• Success criteria
• Results and metrics
• Rationale for choices

Pro Tip: GenAI Explorer's prompt history automatically documents your tests! Export the history for comprehensive records.

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Testing Template

Use This Structure

## Test: [Test Name]

**Date:** [Date]
**Tester:** [Name]

### Configuration
- Model: GPT-4o Mini
- Temperature: 0.7
- Max Tokens: 500
- Top P: 1.0

### Test Cases
1. [Test case 1]
   - Input: [prompt]
   - Expected: [expected output]
   - Actual: [actual output]
   - Pass/Fail: [result]

### Metrics
- Success Rate: 85%
- Avg Response Time: 1.8s
- Avg Cost: $0.006
- Token Usage: 450 avg

### Analysis
- Strengths: [what worked well]
- Weaknesses: [what didn't work]
- Recommendations: [next steps]

### Decision
[ ] Deploy as-is
[ ] Needs optimization
[ ] Try different model

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Advanced Tips

1. A/B Testing

Test two approaches simultaneously:

Group A: GPT-4 Omni, temp 0.5
Group B: GPT-4o Mini, temp 0.7

Compare: Quality, Cost, Speed
Winner: Based on your priorities

2. Prompt Versioning

Track prompt iterations using the built-in prompt history:

v1.0: Original prompt → Saved in history
v1.1: Added examples → Compare with v1.0
v1.2: Clarified format → See improvement
v1.3: Optimized tokens (20% reduction) → Best version

GenAI Explorer automatically saves all versions - just browse your history to compare!

3. Automated Testing

// Run automated tests
testSuite = [
  {prompt: "test1", expected: "result1"},
  {prompt: "test2", expected: "result2"},
  // ...
];

results = runTests(testSuite, model, settings);
report = generateReport(results);

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Related Documentation

• Temperature Guide - Optimize creativity
• Parameters Reference - All settings
• Model Comparison - Choose wisely
• Cost Optimization - Reduce spending
• Sample Prompts - Templates

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Follow these best practices to ensure successful AI model testing and deployment.