category: "AI Ecosystem"
category_id: 15
source_file: "guide/ai-ecosystem.md"

questions:
  - id: "15-001"
    difficulty: "junior"
    profiles: ["junior", "senior", "power"]
    question: "What is the main philosophy behind using complementary AI tools with Claude Code?"
    options:
      a: "Replace Claude Code with better tools"
      b: "Augmentation, not replacement - chain the right tool for each step"
      c: "Use as many tools as possible simultaneously"
      d: "Avoid using Claude Code for coding tasks"
    correct: "b"
    explanation: |
      The philosophy is "augmentation, not replacement."

      Claude Code excels at contextual reasoning and implementation, but has gaps:
      - No real-time web search with source verification
      - No image generation
      - No PowerPoint/slide generation
      - No audio synthesis

      The goal is to chain the right tool for each step of your workflow.
    doc_reference:
      file: "guide/ai-ecosystem.md"
      section: "Introduction"
      anchor: "#introduction"

  - id: "15-002"
    difficulty: "junior"
    profiles: ["junior", "senior", "power"]
    question: "When should you use Perplexity instead of Claude Code?"
    options:
      a: "When implementing features in your codebase"
      b: "When you need deep research with 100+ verified sources"
      c: "When editing multiple files"
      d: "When running tests"
    correct: "b"
    explanation: |
      Perplexity excels at research with verified sources.

      Use Perplexity for:
      - "What's the latest API for X?" (real-time info)
      - "Compare 5 libraries for auth" (sourced comparisons)
      - Deep Research mode synthesizes 100+ sources

      Use Claude Code for:
      - Implementation in your codebase
      - Contextual code explanation
      - Multi-file editing
    doc_reference:
      file: "guide/ai-ecosystem.md"
      section: "1. Perplexity AI"
      anchor: "#1-perplexity-ai-research--sourcing"

  - id: "15-003"
    difficulty: "senior"
    profiles: ["senior", "power"]
    question: "What is the recommended Research-to-Code pipeline?"
    options:
      a: "Claude Code -> Perplexity -> Implementation"
      b: "Perplexity Deep Research -> Export spec.md -> Claude Code implements"
      c: "Google Search -> Copy code -> Paste in project"
      d: "Ask Claude Code to search the web first"
    correct: "b"
    explanation: |
      The Research-to-Code pipeline:

      1. PERPLEXITY (Deep Research)
         - Research best practices with sources
         - Output: 2000-word spec with citations

      2. Export as spec.md

      3. CLAUDE CODE
         - "Implement following spec.md"
         - Output: Working implementation with tests

      This ensures implementation is based on verified, current information.
    doc_reference:
      file: "guide/ai-ecosystem.md"
      section: "Integration Workflow"
      anchor: "#integration-workflow"

  - id: "15-004"
    difficulty: "senior"
    profiles: ["senior", "power"]
    question: "Which tool is best for converting a UI screenshot to React code?"
    options:
      a: "Perplexity"
      b: "Claude Code directly"
      c: "Gemini 2.5 Pro -> then Claude Code for refinement"
      d: "NotebookLM"
    correct: "c"
    explanation: |
      Gemini 2.5 Pro excels at visual understanding:

      Visual-to-Code Pipeline:
      1. GEMINI: Upload screenshot -> Get JSX + Tailwind (90%+ fidelity)
      2. CLAUDE CODE: Refine for your project
         - Add TypeScript types
         - Connect to your components
         - Integrate with your state management

      Claude Code alone has limited image understanding compared to Gemini.
    doc_reference:
      file: "guide/ai-ecosystem.md"
      section: "2. Google Gemini"
      anchor: "#2-google-gemini-visual-understanding"

  - id: "15-005"
    difficulty: "junior"
    profiles: ["junior", "senior", "power"]
    question: "Which tool should you use to create PowerPoint presentations from code documentation?"
    options:
      a: "Claude Code"
      b: "Perplexity"
      c: "Kimi (Moonshot AI)"
      d: "Gemini"
    correct: "c"
    explanation: |
      Kimi (kimi.ai) from Moonshot AI specializes in PPTX generation:

      Features:
      - Native PPTX export (actual slides, not markdown)
      - 128K+ token context (entire codebases)
      - Code-aware layouts with syntax highlighting
      - Excellent for stakeholder decks from technical content

      Workflow: Claude Code generates summary -> Kimi creates presentation
    doc_reference:
      file: "guide/ai-ecosystem.md"
      section: "3. Kimi"
      anchor: "#3-kimi-pptx--long-document-generation"

  - id: "15-006"
    difficulty: "senior"
    profiles: ["senior", "power"]
    question: "What is NotebookLM's unique feature for developer onboarding?"
    options:
      a: "Code execution"
      b: "Audio Overview - generates podcast-style explanations from docs"
      c: "Real-time collaboration"
      d: "Git integration"
    correct: "b"
    explanation: |
      NotebookLM's Audio Overview feature:

      - Upload 50+ documentation files
      - Generates 10-15 minute "podcast"
      - Two AI hosts discuss your codebase
      - Perfect for onboarding or reviewing large systems

      Workflow:
      1. Export docs to combined-docs.md
      2. Upload to NotebookLM
      3. Generate Audio Overview
      4. Listen during commute
      5. Return to Claude Code with deeper understanding
    doc_reference:
      file: "guide/ai-ecosystem.md"
      section: "4. NotebookLM"
      anchor: "#4-notebooklm-synthesis--audio"

  - id: "15-007"
    difficulty: "power"
    profiles: ["power"]
    question: "In the complete workflow pipeline, what is the correct order of phases?"
    options:
      a: "Implementation -> Planning -> Delivery"
      b: "Planning (Perplexity/Gemini/NotebookLM) -> Implementation (Claude Code) -> Delivery (Kimi)"
      c: "Delivery -> Planning -> Implementation"
      d: "All tools simultaneously"
    correct: "b"
    explanation: |
      The complete pipeline has 3 phases:

      PLANNING PHASE:
      - Perplexity: Deep Research -> spec.md
      - Gemini: Diagram Analysis -> mermaid + plan
      - NotebookLM: Doc Synthesis -> audio overview

      IMPLEMENTATION PHASE:
      - Claude Code: Multi-file implementation
      - IDE + Copilot: Inline autocomplete

      DELIVERY PHASE:
      - Claude Code: PR description, release notes
      - Kimi: Stakeholder deck (presentation.pptx)
    doc_reference:
      file: "guide/ai-ecosystem.md"
      section: "7. Workflow Orchestration"
      anchor: "#7-workflow-orchestration"

  - id: "15-008"
    difficulty: "junior"
    profiles: ["junior", "senior", "power"]
    question: "What is v0.dev best used for in the AI ecosystem?"
    options:
      a: "Backend API development"
      b: "Database queries"
      c: "Rapid UI prototyping with Shadcn/Tailwind"
      d: "Security auditing"
    correct: "c"
    explanation: |
      v0.dev excels at rapid UI prototyping:

      - Generates React + Shadcn + Tailwind components
      - Live preview in browser
      - Quick iteration on UI designs

      Workflow:
      1. v0: Prompt -> Get component preview
      2. Export code
      3. Claude Code: Adapt for your project
         - Add TypeScript types
         - Connect to your APIs
         - Integrate with your design system
    doc_reference:
      file: "guide/ai-ecosystem.md"
      section: "6. UI Prototypers"
      anchor: "#6-ui-prototypers-v0-bolt-lovable"

  - id: "15-009"
    difficulty: "senior"
    profiles: ["senior", "power"]
    question: "How does Claude WebSearch compare to Perplexity Pro for research?"
    options:
      a: "Claude WebSearch has more sources (100+)"
      b: "They are identical in functionality"
      c: "Claude WebSearch: 5-10 sources, quick lookups. Perplexity Pro: 100+ sources, comprehensive research"
      d: "Perplexity cannot access real-time data"
    correct: "c"
    explanation: |
      Comparison:

      | Feature | Claude WebSearch | Perplexity Pro |
      |---------|-----------------|----------------|
      | Source count | ~5-10 | 100+ (Deep Research) |
      | Source verification | Basic | Full citations |
      | Best for | Quick lookups | Comprehensive research |
      | Cost | Included | $20/month |

      Recommendation: Use Claude WebSearch for quick factual checks.
      Use Perplexity Deep Research before significant implementations.
    doc_reference:
      file: "guide/ai-ecosystem.md"
      section: "Comparison"
      anchor: "#comparison-claude-websearch-vs-perplexity"

  - id: "15-010"
    difficulty: "power"
    profiles: ["power"]
    question: "What is the 'Minimal Stack' monthly cost recommendation for AI tools?"
    options:
      a: "$200+/month - all Pro subscriptions"
      b: "$40-70/month - Claude Code + Perplexity Pro, free tiers for rest"
      c: "$0 - only free tiers"
      d: "$500+/month - enterprise plans"
    correct: "b"
    explanation: |
      Recommended subscription stacks:

      Minimal Stack ($40-70/month):
      - Claude Code (pay-per-use): $20-50
      - Perplexity Pro: $20
      - Everything else: Free tiers (NotebookLM, Kimi, Gemini free)

      Balanced Stack ($80-110/month):
      - Add Gemini Advanced ($20) and Cursor Pro ($20)

      Cost optimization tips:
      - Use Haiku for simple tasks
      - Batch research sessions in Perplexity
      - Check context usage regularly (/status)
    doc_reference:
      file: "guide/ai-ecosystem.md"
      section: "8. Cost & Subscription Strategy"
      anchor: "#8-cost--subscription-strategy"
