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Learning Paths

Recommended sequences through the curriculum based on your goals.

Foundation Path

Best for engineers new to AI. Covers the essentials before branching out.

  1. LLM Fundamentals → understand tokens, models, and API basics
  2. Prompt Engineering → master prompting techniques and tool calling
  3. RAG → build your first retrieval-augmented generation system
  4. Testing & Evaluation → learn to measure and improve AI systems

Agent Builder Path

For engineers focused on building autonomous AI systems.

  1. LLM Fundamentals + Prompt Engineering (prerequisites)
  2. AI Agents → tool calling, ReAct, multi-agent systems
  3. AI Memory → conversation and entity memory for agents
  4. Model Context Protocol → standardized tool integration

Production Engineer Path

For engineers deploying AI systems at scale.

  1. LLM Fundamentals + Prompt Engineering (prerequisites)
  2. LLM Gateways → routing, caching, and cost management
  3. AI Observability → tracing, metrics, and monitoring
  4. Testing & Evaluation → automated quality assurance

Full Stack AI Path

Complete the entire curriculum in recommended order.

1️⃣

Phase 1: Foundations

LLM Fundamentals → Prompt Engineering → RAG

2️⃣

Phase 2: Agents

AI Agents → MCP → AI Memory

3️⃣

Phase 3: Production

Gateways → Observability → Testing

4️⃣

Phase 4: Tooling

Developer Tools → Agentic Coding