AI Prompt Engineering Mastery - Complete Roadmap

Course Philosophy: Learning by Doing

This course is built around hands-on practice, not theory. Every concept is taught through interactive Jupyter notebooks with real-world projects you can immediately apply.

Level 1: Foundation Labs (2-3 weeks)

Master the core skills that separate professionals from beginners

Notebook 1: Prompt Foundations Available Now

Time: 2-3 hours | Skills: Clarity, specificity, CLEAR framework

Fix 3 broken prompts that everyone struggles with
Learn the CLEAR framework (Context, Length, Examples, Audience, Requirements)
Complete real-world scenarios (job applications, social media, code help)
Take the $500 content challenge

Notebook 2: Context Mastery Coming Soon

Time: 2-3 hours | Skills: Information grounding, domain knowledge

Make AI understand YOUR specific business context
Learn few-shot and zero-shot prompting techniques
Master data integration and knowledge injection
Build domain-specific prompt libraries

Notebook 3: Structured Outputs Coming Soon

Time: 2-3 hours | Skills: JSON, XML, API-ready data generation

Generate clean, parseable data formats
Master schema-driven prompt design
Build API integration workflows
Create data validation pipelines

Notebook 4: Chain-of-Thought Coming Soon

Time: 2-3 hours | Skills: Step-by-step reasoning, logic chains

Implement reasoning frameworks
Build multi-step problem solving
Create verification and validation loops
Master complex analytical tasks

Level 2: Engineering Playground (2-3 weeks)

Build advanced systems that professionals use in production

Notebook 5: Advanced Frameworks Coming Soon

Time: 3-4 hours | Skills: ReAct agents, tool integration

Build AI agents that use external tools
Implement ReAct (Reasoning + Acting) patterns
Create tool-calling workflows
Master agent orchestration

Notebook 6: Prompt Chaining Coming Soon

Time: 3-4 hours | Skills: Multi-step workflows, pipeline design

Chain prompts for complex tasks
Build conditional logic flows
Create error handling and fallbacks
Master workflow optimization

Notebook 7: Self-Critique Systems Coming Soon

Time: 3-4 hours | Skills: Quality loops, self-improvement

Build AI that critiques its own work
Implement iterative improvement cycles
Create quality scoring systems
Master self-correction patterns

Notebook 8: Testing & Evaluation Coming Soon

Time: 3-4 hours | Skills: Benchmarking, optimization, A/B testing

Measure prompt performance scientifically
Build A/B testing frameworks
Create evaluation metrics and benchmarks
Master continuous improvement processes

Level 3: Professional Projects (2-3 weeks)

Ship real applications with measurable business value

Project 1: Customer Support AI Coming Soon

Goal: Reduce response times by 40%

Automated ticket classification and routing
Context-aware response generation
Escalation detection and handoff
Performance monitoring and optimization

Project 2: Content Strategy Engine Coming Soon

Goal: Automated content pipelines

Multi-platform content generation
Brand voice consistency systems
Content calendar automation
Performance tracking and optimization

Project 3: Code Review Assistant Coming Soon

Goal: Improve code quality metrics

Automated code analysis and suggestions
Security vulnerability detection
Performance optimization recommendations
Documentation generation

Project 4: Research Synthesis Bot Coming Soon

Goal: Accelerate literature reviews

Multi-source information synthesis
Citation and reference management
Insight extraction and summarization
Knowledge graph construction

Built-in Learning Tools

Immediate Feedback Systems

Prompt Validator: Scores prompts 0-100% using best practices
Progress Tracker: Tracks mastery across 17 core competencies
Version Control: Git-like system for prompt management
A/B Testing: Compare prompt performance scientifically

Skill Assessment

Pre/post assessments in each notebook
Skill matrices that fill as you progress
Completion certificates for each level
Portfolio documentation for job applications

Production-Ready Code

Error handling and fallback systems
Performance monitoring and optimization
Deployment guides and best practices
Real-world integration examples

Time Investment & ROI

Total Time: 6-9 weeks (10-15 hours per week) Weekly Breakdown:

2-3 hours on new concepts
3-4 hours on hands-on exercises
2-3 hours on projects and practice
1-2 hours on review and optimization

Expected ROI:

Save 10+ hours per week on AI-assisted tasks
Generate professional-quality content in minutes
Build systems that handle routine work automatically
Develop portfolio projects for career advancement

Getting Started

New to prompt engineering? → Start with Foundations Lab

Have some experience? → Take the skill assessment to find your level

Want to see examples first? → Check solutions and examples

Ready to dive in? → Follow the setup guide

This roadmap is updated as new content is released. Star the repository to get notified of new labs and projects.