Learning Paths

Overview

Different learners need different sequencing. These paths organize AI learning so progress feels intentional instead of scattered.

Learning Paths at a glance

Find the focus areas, practical guidance and next steps that match your goals.

Student Track

Move from fundamentals to portfolio-ready AI projects, certifications, and career proof of work.

Developer Track

Layer AI features onto engineering workflows, ship fullstack AI products, and improve technical leverage.

Builder Track

Focus on product thinking, MVP sequencing, and practical AI systems that solve real user problems.

Choose The AI Engineering Path That Matches Your Level

Each path includes a clear sequence, key modules, suggested projects, expected outcomes, and a realistic duration so learners know what to do next.

Beginner Path

Build a strong base in AI engineering by learning the core concepts, using APIs, and finishing a few guided projects that make the fundamentals feel real.

Recommended Sequence

• AI foundations
• Prompt engineering
• LLM app basics
• Embeddings and retrieval basics
• First end-to-end AI project

Expected Outcomes

• Understand core AI engineering concepts
• Build simple LLM-powered apps
• Use prompts more reliably
• Connect frontend, backend, and AI APIs
• Complete 2 to 3 portfolio-ready beginner projects

Modules And Concepts

• LLMs
• tokens
• context windows
• model strengths and limits
• system prompts
• few-shot prompting
• output formatting
• prompt iteration
• API calls
• input and output flow
• basic validation
• frontend and backend integration
• embeddings
• similarity search
• vector basics
• simple retrieval
• feature scoping
• user flow
• testing outputs
• deployment basics

Suggested Projects

Intermediate Path

Move beyond simple prompt apps and learn how to design retrieval, workflow, and system layers that make AI products more useful and reliable.

• Stronger prompt systems
• RAG architecture
• Workflow orchestration
• Production-minded AI apps
• Applied project portfolio
• Build practical RAG applications
• Choose appropriate AI architecture patterns
• Design more reliable AI workflows
• Handle common failure cases
• Ship 2 to 3 intermediate projects with stronger system thinking
• structured prompts
• guardrails
• prompt testing
• reusable prompt patterns
• chunking
• vector databases
• retrieval quality
• context assembly
• chaining steps
• tool use
• validation
• retries
• fallback logic
• architecture patterns
• latency
• cost
• reliability
• observability
• project planning
• documentation
• GitHub workflow
• iteration

Advanced Path

Learn to think like an AI systems engineer by working with agents, evaluation, production tradeoffs, and public proof of work.

• Advanced system design
• Agents and tool use
• Evaluation and optimization
• Multi-component AI systems
• Public proof of work
• Design architecture-first AI systems
• Build agentic and multi-step workflows
• Evaluate and improve AI system quality
• Make better tradeoff decisions in production contexts
• Produce advanced portfolio, GitHub, and showcase-ready work
• system tradeoffs
• orchestration layers
• state
• scaling
• deployment patterns
• planning
• tool calling
• memory
• multi-step execution
• agent safety
• evals
• tracing
• benchmarking
• cost optimization
• latency tuning
• monitoring
• logging
• fallback systems
• human-in-the-loop design
• open source contribution
• hackathons
• case studies
• public project communication

Continue your journey

Connect what you learn here with your next practical step.

Move From Reading To Action

Choose your next learning or building step and put these ideas into practice.