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Syllabus
AI Engineering— full course outline from your library.
AI Engineering
Orientation
AI Engineering — Syllabus & How to Use This Course
Large language models
Next-Token Prediction
Transformers and Attention
Tokenization and Embeddings
Training: From Pretraining to RLHF
Inference: Sampling and the Context Window
Capabilities, Limits, and Scaling
Programming the model
Prompting and Context Engineering
Structured Output
Tool Calling
Retrieval-Augmented Generation
Reasoning Models and Test-Time Compute
Agents and agent loops
What Is an Agent?
ReAct: Reasoning and Acting
Agent Loops: Reflexion, Plan-and-Execute, and Tree Search
Memory and Context Management
Agent Design Patterns
Agent infrastructure
The Model Context Protocol (MCP)
Tool Design
Harnesses and Scaffolds
CLI and Coding Agents
The ecosystem
Agentic Frameworks
Frontier Labs and Models
Flagship Apps: Claude, Gemini, and Codex
The Tooling Landscape
Multi agent systems
Why (and Why Not) Multi-Agent
Orchestration Patterns
Multi-Agent Case Studies
Evaluation and benchmarks
Evaluating LLMs and Agents
The Benchmark Landscape
Observability and Tracing
Papers and capstone
The Canonical Papers
Capstone: Build an Agent
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