Practice and reinforce the concepts from Lesson 16
Transform your learning journey into actionable assets: a personal playbook, portfolio-ready projects, and a 90-day growth plan.
You will create:
Prerequisites: Completed Concepts 10-15, 3+ major projects, document editor (Google Docs/Notion/Markdown), 45 minutes focused time
Download AI3-Template-activity-16-playbook.zip and complete your personal playbook document.
Create timeline showing progression through 4 stages (Copy-Paste -> Understand & Modify -> Orchestrate AI -> AI Collaborator) with project examples for each stage.
Example: "Stage 2 moment: I modified the difficulty selector AI generated to add custom styling."
| Concept | Skills to Check | Mastery Level |
|---|---|---|
| 10A: Four Stages | Articulate current stage, progressed through 2+ stages, recognize stage switching | 0-8: Stage 1-29-16: Stage 2-317-24: Stage 3-425+: Stage 4 |
| 11: Prompting | Clear prompts with context, chain-of-thought reasoning, iteration, task breakdown | |
| 12: Vibe Mastery | Collaboration over dependence, design-first approach, code review, recognize AI limits | |
| 13: Multi-Agent | Used multi-agent workflows, sequential vs parallel, context management | |
| 14: Cost Optimization | Model selection (Haiku/Sonnet/GPT-4), context minimization, local testing | |
| 15: RAG | RAG architecture, vector databases, embeddings, chatbot with external knowledge |
Count checkmarks -> Determine your mastery stage -> Identify 3-5 skills to focus on next.
Identify 3 breakthroughs (when it happened, what changed, example) + biggest current challenge + 5 new skills you couldn't imagine at Lesson 1.
Example: "Breakthrough: During Smart Farm chatbot, I stopped asking AI to 'create a chatbot' and designed conversation flow first."
Document your unique AI development strategies in the playbook template.
| Technique | When to Use | Template Example |
|---|---|---|
| Context-First | Complex features | "Context: Building [app] with [stack]. Current status: [done]. Task: [specific request]" |
| Chain-of-Thought | Complex reasoning | "Before coding, explain: 1) Approach 2) Edge cases 3) Issues. Then implement." |
| Iterative Refinement | Feature polish | Start basic -> Test -> "Improve X by adding Y" -> Repeat |
Document 3-5 techniques with real project examples + common prompt failures and fixes.
6-Step Process: Reproduce bug -> Gather info (DevTools, console.log) -> Form hypothesis -> Test with AI ("Error: [message], Code: [snippet], Tried: [attempts]") -> Implement fix -> Document learning.
Tools: Browser DevTools (console, network, elements), console.log patterns, AI debugging (after 15min self-debug).
Identify 3 common bug types you've mastered (e.g., async/await, undefined variables, CORS).
| Pattern | Use Case | Example |
|---|---|---|
| Sequential Pipeline | Dependent tasks | Design -> Implement -> Test (output passes to next agent) |
| Parallel Execution | Independent polish | UI + Docs + Tests run simultaneously |
| Hybrid | Custom workflows | Design sequentially, polish in parallel |
Document context management strategies (master context doc, output passing) + when NOT to use multi-agent (simple tasks under 15min).
| Model Tier | Use Cases | Token Savings |
|---|---|---|
| Haiku/GPT-3.5 | Syntax fixes, simple edits, docs generation | 10x cheaper |
| Sonnet/GPT-4 | Feature dev, debugging, system design (90% of work) | Balanced |
| Opus/GPT-4 Turbo | Novel features, complex reasoning (only when needed) | Use sparingly |
Context Minimization: Share only relevant code (80% savings), use summaries not full docs (90% savings), reference previous context.
Workflow: Planning (free tools) -> Implementation (Sonnet) -> Polish (Haiku/Sonnet) -> Verification (Haiku).
Select 3 projects for professional showcase and prepare portfolio materials.
| Project Component | Checklist |
|---|---|
| Code Quality | Remove console.log, add error handling, improve naming, add comments, run linter |
| Documentation | Comprehensive README, setup instructions, screenshots, tech stack, AI credit disclosure |
| Professional Polish | Mobile testing, loading states, error messages, accessibility, performance optimization |
| Deployment | Deploy to Vercel/Netlify, env variables, test production, custom domain (optional) |
For each of 3 projects: Current status, skills demonstrated, live demo URL, 30-second pitch.
Pitch Example: "RAG-powered chatbot that helps farmers diagnose crop diseases using vector embeddings to search 500+ documents, reducing diagnosis time from 2 hours to 2 minutes."
Write complete README for your best project with sections: Demo (screenshot, live site), Features (3-5 with technical implementation), Tech Stack (frontend/backend/AI/deployment), What I Learned (3 technical skills + biggest challenge), Getting Started (prerequisites, installation, env variables), AI Collaboration (honest role disclosure: "I designed architecture and conversation flow; AI helped implement components"), Future Enhancements.
| Segment | Time | Content |
|---|---|---|
| Problem | 15s | What problem does project solve? |
| Solution Demo | 60s | 4-step walkthrough showing key features |
| Technical Depth | 30s | Architecture, multi-agent orchestration, cost optimization |
| Impact & Growth | 15s | Key learning, Stage 1->3-4 progression |
Prepare Q&A responses: How you used AI, biggest challenge, scalability plan, what you'd do differently.
Create continuous learning roadmap with SMART goals, resources, and community contributions.
Set 3 goals using framework: Specific (what exactly?), Measurable (how to verify?), Achievable (resources needed?), Relevant (why matters?), Time-bound (deadline?).
Example Goal: "Master Production RAG - Build/deploy 2 RAG apps handling 100+ queries/day with under 2s latency by [90 days]. Resources: LangChain docs, Pinecone tutorials, Vercel/Railway."
Action steps: 8-12 week breakdown (e.g., Week 1-2: Study architectures, Week 3-4: Build app #1, etc.)
| Resource Type | Examples | Time Commitment |
|---|---|---|
| Daily/Weekly | The Batch newsletter, AI Twitter (@AnthropicAI, @OpenAI), r/ClaudeAI | 15min daily, 1hr weekly |
| Deep Learning | LangChain docs, AI Engineer Summit talks, experimental projects | 4-6hrs monthly |
| Quarterly Review | Evaluate valuable resources, remove unused, add new based on interests | Every 3 months |
Learning Schedule: Daily (check AI Twitter, skim article) -> Weekly (newsletter, forum, try new tool) -> Monthly (docs, talks, build project, write blog post).
| Contribution Goal | Action Plan | Success Metric |
|---|---|---|
| Teach 1 Person | 4 weekly 1hr sessions (vibe engineering intro -> chatbot -> features -> deployment) | They build working AI app independently |
| Open Source | Find "good first issue," contribute within 30 days (bug fix/docs/feature/tutorial) | PR merged |
| Content Creation | 2-3 pieces (blog: vibe journey, technical guide, playbook) over 3 months | Published + community engagement |
Accountability: Calendar reminders, public goal sharing (Twitter/LinkedIn), accountability partner, weekly review.
Submit a comprehensive playbook document (Google Doc/Notion/Markdown) titled "[Your Name] - Vibe Engineering Mastery Playbook" with:
| Section | Required Elements | Success Criteria |
|---|---|---|
| Journey Assessment | Stage progression map, skills checklist (6 concepts), 3 breakthroughs, current challenge | Honest self-assessment, specific project examples |
| Personal Playbook | 3-5 prompting techniques, debugging workflow, 2-3 multi-agent patterns, cost optimization matrix | Real project examples, reusable templates |
| Portfolio Preparation | 3 selected projects with improvement plans, 1 complete README, 2-min demo script | Portfolio-ready quality, professional polish |
| Continuous Growth | 3 SMART goals (90 days), 5+ resources, community contribution plan, learning schedule | Actionable plans with deadlines, accountability measures |
Format: Google Doc (view access) / Notion (public link) / GitHub Markdown / PDF | Length: 8-12 pages | Tone: Specific examples, honest reflection, actionable next steps
Extension: Video reflection (5min), playbook visualization, teach concept to friend, publish publicly (optional)