AI for Developer Productivity: What Actually Works

productivity_research:
  github_study:
    finding: 55% faster task completion
    context: Specific controlled task (HTTP server)
    caveat: Not representative of all work

  real_world_observations:
    high_impact:
      - Boilerplate generation
      - Test scaffolding
      - Documentation
      - Familiar patterns

    low_impact:
      - Novel algorithms
      - Complex debugging
      - Architecture decisions
      - Domain-specific logic

    sometimes_negative:
      - When suggestions are wrong
      - When review overhead exceeds value
      - When developers over-trust output

developer_variation:
  who_benefits_most:
    - Developers working with unfamiliar APIs
    - Those writing repetitive code
    - Polyglot developers switching languages
    - Those comfortable reviewing AI output

  who_benefits_less:
    - Deep domain experts (AI can't match knowledge)
    - Those working on novel problems
    - Those in heavily regulated environments
    - Security-focused developers

high_value_patterns:
  boilerplate_generation:
    task: Create CRUD endpoints, model definitions
    value: Eliminates tedious typing
    quality: Usually good enough to use

  test_generation:
    task: Create test scaffolding from implementation
    value: Faster test coverage
    quality: Good starting point, needs refinement

  documentation:
    task: Docstrings, READMEs, comments
    value: Reduces documentation burden
    quality: Good first draft

  api_exploration:
    task: "How do I use this library?"
    value: Faster than reading docs
    quality: Usually accurate for popular libraries

  code_translation:
    task: Convert between languages
    value: Accelerates migration
    quality: Good for syntax, check semantics

effective_workflows:
  comment_first:
    approach: Write detailed comment, let AI implement
    example: "// Function to validate email with regex, return bool"
    benefit: AI has clear specification

  test_first:
    approach: Write test, let AI write implementation
    benefit: Tests validate AI output

  iterative_refinement:
    approach: Accept partial, modify, continue
    benefit: Combines AI speed with human judgment

  review_rigorously:
    approach: Treat AI output as junior developer code
    benefit: Catches errors before they ship

productivity_anti_patterns:
  blind_acceptance:
    pattern: Accept suggestions without review
    problem: Subtle bugs, security issues
    result: Debugging time exceeds generation time

  complex_prompting:
    pattern: Long prompts trying to specify everything
    problem: Diminishing returns, fragile
    result: Simpler prompts often work better

  fighting_the_ai:
    pattern: Repeatedly rejecting to get perfect output
    problem: Time spent exceeds manual writing
    result: Net negative productivity

  over_reliance:
    pattern: Using AI for everything
    problem: Skill atrophy, wrong tool for job
    result: Worse outcomes for complex tasks

skip_ai_for:
  security_critical:
    - Authentication logic
    - Cryptography
    - Access control
    why: AI may introduce vulnerabilities

  performance_critical:
    - Hot paths
    - Algorithms with specific complexity requirements
    why: AI optimizes for readability, not performance

  domain_complex:
    - Business logic requiring deep context
    - Regulatory compliance code
    why: AI lacks necessary context

  already_know_it:
    - Simple code you can type quickly
    why: Review overhead exceeds benefit

adoption_approach:
  start_small:
    - Opt-in for interested developers
    - Specific use cases
    - Gather feedback

  measure_impact:
    - Task completion time
    - Code review findings
    - Developer satisfaction
    - Bug rates

  share_learnings:
    - What works, what doesn't
    - Effective prompts
    - Review techniques

  adjust_workflow:
    - Code review focus areas
    - Testing expectations
    - Documentation practices

code_review_for_ai:
  additional_focus:
    - Plausible but wrong logic
    - Security vulnerabilities
    - Edge cases
    - Performance implications

  questions_to_ask:
    - "Does this handle null/empty cases?"
    - "Is this the right algorithm?"
    - "Are there security implications?"
    - "Does this match our patterns?"

  red_flags:
    - Unusually verbose solutions
    - Unfamiliar patterns
    - Missing error handling
    - Suspicious confidence

productivity_metrics:
  task_level:
    - Time to complete specific tasks
    - Code volume for similar tasks
    - Number of iterations needed

  quality_level:
    - Bugs per feature
    - Code review iteration count
    - Test coverage

  satisfaction:
    - Developer survey scores
    - Tool usage rates
    - Retention of tool usage

  caution:
    - Avoid measuring lines of code
    - Avoid comparing developers
    - Context matters enormously