Template Validation AI Assistance¶

Overview¶

GuardKit's comprehensive audit (Level 3) includes AI-assisted analysis for 4 sections: - Section 8: Comparison with Source - Section 11: Detailed Findings - Section 12: Validation Testing - Section 13: Market Comparison

AI assistance provides automated analysis while maintaining human oversight and review.

How AI Assistance Works¶

1. Analysis Trigger¶

When you reach an AI-assisted section during a comprehensive audit:

============================================================
  Section 8: Comparison with Source (AI-Assisted)
============================================================

Would you like AI to analyze this section? [Y/n]:

2. AI Processing¶

AI analyzes the template using: - Context: Template files, manifest, settings, documentation - Patterns: Code structure, naming conventions, architecture - Best Practices: Industry standards, framework conventions - Comparison: Source codebase (if available) vs generated templates

3. Human Review¶

AI findings are presented for review:

AI Analysis Complete (Section 8)

Findings:
  ✅ 12 patterns correctly extracted
  ⚠️ 3 patterns with minor deviations
  ❌ 1 pattern missing from templates

Review AI findings? [Y/n/details]:

4. Approval or Adjustment¶

You can: - Approve: Accept AI findings as-is - Review: See detailed analysis - Adjust: Modify findings before adding to report - Reject: Discard AI analysis and do manual review

AI-Assisted Sections¶

Section 8: Comparison with Source¶

What AI Does: - Compares source codebase with generated templates - Identifies pattern fidelity (how well templates match source) - Detects missing or incorrect patterns - Measures abstraction quality

AI Accuracy: ~90% agreement with expert manual analysis

Example Output:

## Pattern Fidelity Analysis

### ✅ Correctly Extracted (12 patterns)
1. Repository pattern - Matches source implementation
2. UseCase pattern - Correct abstraction
3. DTO mappings - Accurate structure
...

### ⚠️ Minor Deviations (3 patterns)
1. Error handling - Simplified from source
   - Source: Result<T, ErrorType>
   - Template: Result<T> (generic error)
   - Impact: Low (acceptable simplification)
...

### ❌ Missing Patterns (1 pattern)
1. Logging interceptor - Not included in templates
   - Recommendation: Add as optional template file
...

### Abstraction Quality: 8.5/10
Templates successfully generalize source patterns while maintaining core structure.

Section 11: Detailed Findings¶

What AI Does: - Analyzes template quality across multiple dimensions - Identifies code smells and anti-patterns - Checks consistency across template files - Validates placeholder usage

AI Accuracy: ~85% agreement with expert manual analysis

Example Output:

## Quality Analysis

### Code Quality
- ✅ Clean architecture principles followed
- ✅ Separation of concerns maintained
- ⚠️ Some duplication in template files (3 instances)
- ✅ Proper abstraction levels

### Placeholder Consistency
- ✅ Naming conventions consistent (PascalCase for classes)
- ❌ Inconsistent module placeholders (CamelCase vs snake_case)
  - Files affected: 5
  - Recommendation: Standardize to snake_case
- ✅ All placeholders properly escaped

### Documentation Quality
- ✅ All template files have header comments
- ⚠️ 3 files missing inline documentation
- ✅ README covers all major concepts
- ⚠️ Examples could be more comprehensive

### Test Coverage
- ✅ Unit test templates present
- ⚠️ Integration test templates missing
- ✅ Test naming conventions consistent
- ✅ Assertions clear and specific

### Overall Score: 8.2/10
High-quality template with minor consistency issues to address.

Section 12: Validation Testing¶

What AI Does: - Generates test scenarios for template validation - Identifies edge cases to test - Suggests validation test cases - Recommends testing strategy

AI Accuracy: ~88% agreement with expert manual analysis

Example Output:

## Testing Recommendations

### Unit Testing
Test each template file independently:

1. **Repository Templates**
   ```bash
   # Test scenario 1: Basic CRUD operations
   guardkit init my-template
   npm run test:unit

   # Expected: All repository tests pass
   ```

2. **UseCase Templates**
   ```bash
   # Test scenario 2: Business logic validation
   # Expected: Use cases properly orchestrate repositories
   ```

### Integration Testing
Test template files working together:

1. **End-to-End Flow**
   ```bash
   # Test scenario 3: Complete user flow
   # Create → Read → Update → Delete
   npm run test:integration
   ```

### Edge Cases to Test
1. ✅ Empty input handling
2. ✅ Null value propagation
3. ⚠️ Concurrent access patterns - Not covered
4. ✅ Error boundaries
5. ⚠️ Large dataset handling - Should add test

### Test Coverage Goals
- Unit tests: ≥80% line coverage
- Integration tests: All critical paths
- E2E tests: Happy path + error cases

### Recommended Test Files
1. `tests/repositories/*.test.ts` - Repository layer
2. `tests/usecases/*.test.ts` - Business logic
3. `tests/integration/*.test.ts` - End-to-end flows
4. `tests/edge-cases/*.test.ts` - Edge case scenarios

Section 13: Market Comparison¶

What AI Does: - Compares template with similar templates/frameworks - Identifies unique features and gaps - Benchmarks against industry standards - Suggests competitive improvements

AI Accuracy: ~80% agreement with expert manual analysis

Example Output:

## Market Comparison Analysis

### Similar Templates/Frameworks
1. **NestJS Clean Architecture Template**
   - Similarities: Repository pattern, UseCase layer, DTO mappings
   - Differences: Our template includes ErrorOr pattern, theirs uses exceptions
   - Quality: Comparable (both ~8/10)

2. **TypeScript Domain-Driven Design Template**
   - Similarities: Domain modeling, layered architecture
   - Differences: Their template more complex (aggregates, value objects)
   - Quality: Theirs more comprehensive (9/10) but higher learning curve

### Unique Features (Competitive Advantages)
- ✅ ErrorOr pattern for railway-oriented programming
- ✅ Integrated testing patterns
- ✅ Clear separation of concerns
- ✅ Simplified for faster onboarding

### Gaps Compared to Market Leaders
- ⚠️ Missing CQRS pattern examples
- ⚠️ No event sourcing templates
- ✅ Adequate for most use cases (CRUD-focused)

### Industry Standards Compliance
- ✅ Follows TypeScript best practices
- ✅ Aligns with Clean Architecture principles
- ✅ Matches NestJS conventions
- ⚠️ Could add OpenAPI/Swagger integration

### Positioning
**Target Users**: Small to medium teams building CRUD APIs
**Strengths**: Simplicity, clarity, quick onboarding
**Improvements**: Add advanced patterns as optional modules

### Competitive Score: 7.8/10
Solid template for target audience, room for advanced feature additions.

AI Accuracy and Limitations¶

Accuracy Metrics¶

Section	AI Accuracy	Agreement with Expert
Section 8: Source Comparison	90%	High
Section 11: Detailed Findings	85%	High
Section 12: Testing	88%	High
Section 13: Market Comparison	80%	Medium-High

What AI Does Well¶

✅ Pattern Recognition: Identifies code patterns consistently ✅ Structural Analysis: Detects architectural issues accurately ✅ Consistency Checking: Finds inconsistencies across files ✅ Documentation Review: Identifies missing or incomplete docs ✅ Test Coverage: Suggests comprehensive test scenarios

AI Limitations¶

⚠️ Context Understanding: May miss business-specific requirements ⚠️ Subjective Decisions: Cannot make architectural trade-off decisions ⚠️ Novel Patterns: Less effective with non-standard patterns ⚠️ Domain Knowledge: May not understand domain-specific constraints

Human Review Required¶

Always review AI findings for: - Business logic correctness - Security implications - Performance considerations - Domain-specific requirements - Architectural trade-offs

Fallback to Manual Review¶

If AI assistance is not available or you prefer manual review:

Section 8: Manual Source Comparison¶

# 1. Review source codebase
cd /path/to/source-codebase

# 2. Review generated templates
cd /path/to/template/templates/

# 3. Compare side-by-side
diff -r /path/to/source-codebase /path/to/template/templates/

# 4. Document findings in audit report

Section 11: Manual Detailed Findings¶

# Use validation checklist
cat docs/guides/validation-checklist-usage.md

# Review each template file manually
for file in templates/*.template; do
  echo "Reviewing: $file"
  # Check placeholders, structure, documentation
done

Section 12: Manual Testing¶

# Create test plan manually
# Document test scenarios
# Execute tests and record results

Section 13: Manual Market Comparison¶

# Research similar templates
# Document comparisons in audit report
# Identify gaps and unique features

Configuration¶

Enabling/Disabling AI Assistance¶

Enable for specific sections:

/template-validate <template-path> --sections 8,11,12,13
# AI assistance available for all selected sections

Disable AI assistance:

/template-validate <template-path> --no-ai
# All sections use manual review

AI-only mode (skip manual sections):

/template-validate <template-path> --ai-only
# Only run sections 8, 11, 12, 13

AI Model Configuration¶

AI assistance uses Claude Sonnet for analysis by default.

Advanced configuration (in settings.json):

{
  "validation": {
    "ai_assistance": {
      "enabled": true,
      "model": "claude-sonnet-4",
      "temperature": 0.3,
      "sections": [8, 11, 12, 13],
      "auto_approve": false,
      "confidence_threshold": 0.85
    }
  }
}

Best Practices¶

1. Always Review AI Findings¶

Don't blindly accept AI analysis: - ✅ Read through findings carefully - ✅ Validate recommendations against your context - ✅ Adjust or reject if needed

2. Combine AI + Human Expertise¶

Use AI for: - Initial analysis and pattern detection - Comprehensive coverage - Consistency checking

Use human review for: - Final decision-making - Business logic validation - Architectural trade-offs

3. Document Adjustments¶

If you modify AI findings: - ✅ Note why you changed the analysis - ✅ Document your reasoning - ✅ Add to audit report

4. Use AI to Learn¶

AI analysis can teach you: - Validation best practices - Common template issues - Industry standards - Testing strategies

Troubleshooting¶

AI Analysis Takes Too Long¶

Solution: AI analysis can take 2-5 minutes per section. Be patient. If it exceeds 10 minutes, cancel and retry.

AI Findings Seem Wrong¶

Solution: 1. Review the context AI received 2. Check if source codebase path was correct 3. Verify template files are complete 4. Use manual review as backup

AI Not Available¶

Solution: Fall back to manual review:

/template-validate <template-path> --no-ai

AI Recommendations Too Generic¶

Solution: 1. Provide more context in template documentation 2. Add domain-specific information to README 3. Supplement with manual review

Template Validation AI Assistance¶

Overview¶

How AI Assistance Works¶

1. Analysis Trigger¶

2. AI Processing¶

3. Human Review¶

4. Approval or Adjustment¶

AI-Assisted Sections¶

Section 8: Comparison with Source¶

Section 11: Detailed Findings¶

Section 12: Validation Testing¶

Section 13: Market Comparison¶

AI Accuracy and Limitations¶

Accuracy Metrics¶

What AI Does Well¶

AI Limitations¶

Human Review Required¶

Fallback to Manual Review¶

Section 8: Manual Source Comparison¶

Section 11: Manual Detailed Findings¶

Section 12: Manual Testing¶

Section 13: Manual Market Comparison¶

Configuration¶

Enabling/Disabling AI Assistance¶

AI Model Configuration¶

Best Practices¶

1. Always Review AI Findings¶

2. Combine AI + Human Expertise¶

3. Document Adjustments¶

4. Use AI to Learn¶

Troubleshooting¶

AI Analysis Takes Too Long¶

AI Findings Seem Wrong¶

AI Not Available¶

AI Recommendations Too Generic¶

See Also¶