Building Effective AI Teams

ai_team_structures:
  embedded_model:
    description: "AI engineers embedded in product teams"
    best_for: "AI-enhanced features in existing products"
    pros:
      - Close to product context
      - Fast iteration
      - Clear ownership
    cons:
      - Skill isolation
      - Inconsistent practices
      - Duplication

  platform_model:
    description: "Central AI platform team serving product teams"
    best_for: "Multiple products needing AI capabilities"
    pros:
      - Shared infrastructure
      - Consistent practices
      - Deep expertise
    cons:
      - Can become bottleneck
      - Distant from product needs
      - Prioritization conflicts

  hybrid_model:
    description: "Platform + embedded engineers"
    best_for: "Organizations scaling AI"
    structure:
      - Platform team builds tools and infrastructure
      - Embedded AI engineers use platform, customize
      - Regular sync between groups

ai_team_roles:
  core_roles:
    ai_engineer:
      focus: "Build AI-powered features"
      skills:
        - LLM integration
        - Prompt engineering
        - RAG systems
        - Evaluation

    ml_engineer:
      focus: "ML infrastructure and optimization"
      skills:
        - Model fine-tuning
        - MLOps
        - Performance optimization
        - Self-hosting

    data_engineer:
      focus: "Data pipelines for AI"
      skills:
        - ETL for training/RAG
        - Vector databases
        - Data quality

  supporting_roles:
    product_manager:
      focus: "AI product strategy"
      needs: "AI literacy, evaluation mindset"

    designer:
      focus: "AI UX patterns"
      needs: "Understanding AI capabilities and limitations"

    domain_expert:
      focus: "Quality evaluation, edge cases"
      needs: "Deep domain knowledge"

ai_engineer_skills:
  must_have:
    - Strong software engineering fundamentals
    - API integration experience
    - Understanding of LLM capabilities/limitations
    - Prompt engineering proficiency
    - Evaluation mindset

  nice_to_have:
    - ML/DL background
    - RAG system experience
    - Fine-tuning experience
    - Production AI experience

  red_flags:
    - Only knows one framework/model
    - Can't explain how LLMs work conceptually
    - No evaluation methodology
    - Only demo experience, no production

ai_interview_areas:
  system_design:
    - "Design a RAG system for customer support"
    - "How would you handle hallucinations?"
    - "Design multi-model routing"

  prompt_engineering:
    - "Improve this prompt for edge cases"
    - "How do you evaluate prompt quality?"
    - "Explain few-shot vs fine-tuning tradeoffs"

  production_experience:
    - "How do you monitor AI quality in production?"
    - "Describe a production incident and resolution"
    - "Cost optimization strategies"

  evaluation:
    - "How would you measure success for this feature?"
    - "Design an evaluation suite"
    - "When do you involve human evaluation?"

ai_development_workflow:
  discovery:
    - Define success metrics upfront
    - Identify edge cases early
    - Assess data requirements
    - Evaluate build vs buy

  prototyping:
    - Quick prompt experiments
    - Test with real examples
    - Involve domain experts
    - Document what works and doesn't

  development:
    - Build evaluation suite first
    - Version prompts like code
    - Test across model updates
    - Document prompt decisions

  production:
    - Gradual rollout with monitoring
    - A/B test against baselines
    - Track quality metrics
    - Feedback loop to improvement

knowledge_sharing:
  prompt_library:
    purpose: "Reusable prompt patterns"
    includes:
      - Tested prompts with examples
      - Edge case documentation
      - Performance notes

  evaluation_datasets:
    purpose: "Shared test cases"
    includes:
      - Golden examples
      - Edge cases
      - Failure cases

  incident_reviews:
    purpose: "Learn from failures"
    includes:
      - What went wrong
      - Detection method
      - Resolution
      - Prevention

ai_team_pitfalls:
  over_centralization:
    symptom: "AI team becomes bottleneck"
    solution: "Enable product teams with tools and training"

  under_investment_in_eval:
    symptom: "Can't measure quality, ship broken features"
    solution: "Evaluation is not optional—staff it"

  demo_driven_development:
    symptom: "Great demos, broken production"
    solution: "Test edge cases, measure real usage"

  ml_research_mindset:
    symptom: "Optimizing benchmarks nobody cares about"
    solution: "Product metrics over model metrics"

  no_domain_expertise:
    symptom: "Plausible but wrong outputs"
    solution: "Include domain experts in development"

scaling_ai_teams:
  startup_5_10_people:
    structure: "Full-stack AI engineers"
    focus: "Ship features, learn fast"

  growth_20_50_people:
    structure: "Embedded + shared tools"
    focus: "Consistency, reusable components"

  scale_100_plus:
    structure: "Platform team + embedded specialists"
    focus: "Efficiency, governance, quality"

  key_scaling_challenges:
    - Maintaining quality as team grows
    - Knowledge transfer
    - Consistent evaluation standards
    - Cost management