ai-development-guide

name	ai-development-guide
description	Technical decision criteria, anti-pattern detection, debugging techniques, and quality check workflow. Use when making technical decisions, detecting code smells, or performing quality assurance.

AI Developer Guide - Technical Decision Criteria and Anti-pattern Collection

Technical Anti-patterns (Red Flag Patterns)

Immediately stop and reconsider design when detecting the following patterns:

Code Quality Anti-patterns

1. Writing similar code 3 or more times - Violates Rule of Three
2. Multiple responsibilities mixed in a single file - Violates Single Responsibility Principle (SRP)
3. Defining same content in multiple files - Violates DRY principle
4. Making changes without checking dependencies - Potential for unexpected impacts
5. Disabling code with comments - Should use version control
6. Error suppression - Hiding problems creates technical debt
7. Bypassing safety mechanisms (type systems, validation, contracts) - Circumventing language's correctness guarantees

Design Anti-patterns

• "Make it work for now" thinking - Accumulation of technical debt
• Patchwork implementation - Unplanned additions to existing code
• Optimistic implementation of uncertain technology - Designing unknown elements assuming "it'll probably work"
• Symptomatic fixes - Surface-level fixes that don't solve root causes
• Unplanned large-scale changes - Lack of incremental approach

Fail-Fast Fallback Design Principles

Core Principle

Prioritize primary code reliability over fallback implementations. In distributed systems, excessive fallback mechanisms can mask errors and make debugging difficult.

Implementation Guidelines

Default Approach

• Prohibit unconditional fallbacks: Do not automatically return default values on errors
• Make failures explicit: Errors should be visible and traceable
• Preserve error context: Include original error information when re-throwing

When Fallbacks Are Acceptable

• Only with explicit Design Doc approval: Document why fallback is necessary
• Business-critical continuity: When partial functionality is better than none
• Graceful degradation paths: Clearly defined degraded service levels

Layer Responsibilities

• Infrastructure Layer:

  • Always throw errors upward
  • No business logic decisions
  • Provide detailed error context

• Application Layer:

  • Make business-driven error handling decisions
  • Implement fallbacks only when specified in requirements
  • Log all fallback activations for monitoring

Error Masking Detection

Review Triggers (require design review):

• Writing 3rd error handler in the same feature
• Multiple error handling blocks in single function/method
• Nested error handling structures
• Error handlers that return default values without logging

Before Implementing Any Fallback:

1. Verify Design Doc explicitly defines this fallback
2. Document the business justification
3. Ensure error is logged with full context
4. Add monitoring/alerting for fallback activation

Implementation Pattern

Core principle: Make errors explicit with full context. Never hide errors with silent fallbacks.

❌ AVOID: Silent fallback that hides errors
    <handle error>:
        return DEFAULT_VALUE  // Error hidden, debugging impossible

✅ PREFERRED: Explicit failure with context
    <handle error>:
        log_error('Operation failed', context, error)
        <propagate error>  // Re-throw exception, return Error, return error tuple

Adaptation: Use language-appropriate error handling (exceptions, Result types, error tuples, etc.)

Rule of Three - Criteria for Code Duplication

How to handle duplicate code based on Martin Fowler's "Refactoring":

Duplication Count	Action	Reason
1st time	Inline implementation	Cannot predict future changes
2nd time	Consider future consolidation	Pattern beginning to emerge
3rd time	Implement commonalization	Pattern established

Criteria for Commonalization

Cases for Commonalization

• Business logic duplication
• Complex processing algorithms
• Areas likely requiring bulk changes
• Validation rules

Cases to Avoid Commonalization

• Accidental matches (coincidentally same code)
• Possibility of evolving in different directions
• Significant readability decrease from commonalization
• Simple helpers in test code

Implementation Example

// ❌ Immediate commonalization on 1st duplication
validateUserEmail(email) { /* ... */ }
validateContactEmail(email) { /* ... */ }

// ✅ Commonalize on 3rd occurrence with context parameter
validateEmail(email, context) { /* ... */ }
// context: 'user' | 'contact' | 'admin'

Adaptation: Use appropriate abstraction for your codebase (functions, classes, modules, configuration)

Common Failure Patterns and Avoidance Methods

Pattern 1: Error Fix Chain

Symptom: Fixing one error causes new errors Cause: Surface-level fixes without understanding root cause Avoidance: Identify root cause with 5 Whys before fixing

Pattern 2: Circumventing Correctness Guarantees

Symptom: Bypassing safety mechanisms (type systems, validation, contracts) Cause: Impulse to avoid correctness errors Avoidance: Use language-appropriate safety mechanisms (static checking, runtime validation, contracts, assertions)

Pattern 3: Implementation Without Sufficient Testing

Symptom: Many bugs after implementation Cause: Ignoring Red-Green-Refactor process Avoidance: Always start with failing tests

Pattern 4: Ignoring Technical Uncertainty

Symptom: Frequent unexpected errors when introducing new technology Cause: Assuming "it should work according to official documentation" without prior investigation Avoidance:

• Record certainty evaluation at the beginning of task files

  Certainty: low (Reason: no examples of MCP connection found)
  Exploratory implementation: true
  Fallback: use conventional API
  

• For low certainty cases, create minimal verification code first

Pattern 5: Insufficient Existing Code Investigation

Symptom: Duplicate implementations, architecture inconsistency, integration failures Cause: Insufficient understanding of existing code before implementation Avoidance Methods:

• Before implementation, always search for similar functionality (using domain, responsibility, configuration patterns as keywords)
• Similar functionality found → Use that implementation (do not create new implementation)
• Similar functionality is technical debt → Create ADR improvement proposal before implementation
• No similar functionality exists → Implement new functionality following existing design philosophy
• Record all decisions and rationale in "Existing Codebase Analysis" section of Design Doc

Debugging Techniques

1. Error Analysis Procedure

1. Read error message (first line) accurately
2. Focus on first and last of stack trace
3. Identify first line where your code appears

2. 5 Whys - Root Cause Analysis

Example:
Symptom: Build error
Why1: Contract definitions don't match → Why2: Interface was updated
Why3: Dependency change → Why4: Package update impact
Why5: Major version upgrade with breaking changes
Root cause: Inappropriate version specification in dependency manifest

3. Minimal Reproduction Code

To isolate problems, attempt reproduction with minimal code:

• Remove unrelated parts
• Replace external dependencies with mocks
• Create minimal configuration that reproduces problem

4. Debug Log Output

Pattern: Structured logging with context
{
  context: 'operation-name',
  input: { relevant, input, data },
  state: currentState,
  timestamp: current_time_ISO8601
}

Key elements:
- Operation context (what is being executed)
- Input data (what was received)
- Current state (relevant state variables)
- Timestamp (for correlation)

Quality Check Workflow

Universal quality assurance phases applicable to all languages:

Phase 1: Static Analysis

1. Code Style Checking: Verify adherence to style guidelines
2. Code Formatting: Ensure consistent formatting
3. Unused Code Detection: Identify dead code and unused imports/variables
4. Static Type Checking: Verify type correctness (for statically typed languages)
5. Static Analysis: Detect potential bugs, security issues, code smells

Phase 2: Build Verification

1. Compilation/Build: Verify code builds successfully (for compiled languages)
2. Dependency Resolution: Ensure all dependencies are available and compatible
3. Resource Validation: Check configuration files, assets are valid

Phase 3: Testing

1. Unit Tests: Run all unit tests
2. Integration Tests: Run integration tests
3. Test Coverage: Measure and verify coverage meets standards
4. E2E Tests: Run end-to-end tests

Phase 4: Final Quality Gate

All checks must pass before proceeding:

• Zero static analysis errors
• Build succeeds
• All tests pass
• Coverage meets threshold

Quality Check Pattern (Language-Agnostic)

Workflow:
1. Format check → 2. Lint/Style → 3. Static analysis →
4. Build/Compile → 5. Unit tests → 6. Coverage check →
7. Integration tests → 8. Final gate

Auto-fix capabilities (when available):
- Format auto-fix
- Lint auto-fix
- Dependency/import organization
- Simple code smell corrections

Situations Requiring Technical Decisions

Timing of Abstraction

• Extract patterns after writing concrete implementation 3 times
• Be conscious of YAGNI, implement only currently needed features
• Prioritize current simplicity over future extensibility

Performance vs Readability

• Prioritize readability unless clear bottleneck exists
• Measure before optimizing (don't guess, measure)
• Document reason with comments when optimizing

Granularity of Contracts and Interfaces

• Overly detailed contracts reduce maintainability
• Design interfaces that appropriately express domain
• Use abstraction mechanisms to reduce duplication

Continuous Improvement Mindset

• Humility: Perfect code doesn't exist, welcome feedback
• Courage: Execute necessary refactoring boldly
• Transparency: Clearly document technical decision reasoning

Implementation Completeness Assurance

Impact Analysis: Mandatory 3-Stage Process

Complete these stages sequentially before any implementation:

1. Discovery - Identify all affected code:

• Implementation references (imports, calls, instantiations)
• Interface dependencies (contracts, types, data structures)
• Test coverage
• Configuration (build configs, env settings, feature flags)
• Documentation (comments, docs, diagrams)

2. Understanding - Analyze each discovered location:

• Role and purpose in the system
• Dependency direction (consumer or provider)
• Data flow (origin → transformations → destination)
• Coupling strength

3. Identification - Produce structured report:

## Impact Analysis
### Direct Impact
- [Unit]: [Reason and modification needed]

### Indirect Impact
- [System]: [Integration path → reason]

### Data Flow
[Source] → [Transformation] → [Consumer]

### Risk Assessment
- High: [Complex dependencies, fragile areas]
- Medium: [Moderate coupling, test gaps]
- Low: [Isolated, well-tested areas]

### Implementation Order
1. [Start with lowest risk or deepest dependency]
2. [...]

Critical: Do not implement until all 3 stages are documented

Unused Code Deletion

When unused code is detected:

• Will it be used in this work? Yes → Implement now | No → Delete now (Git preserves)
• Applies to: Code, tests, docs, configs, assets

Existing Code Modification

In use? No → Delete
       Yes → Working? No → Delete + Reimplement
                     Yes → Fix/Extend

Principle: Prefer clean implementation over patching broken code