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🤖 AI Engineering Essentials

Build production LLM applications — prompting, RAG, agents and tool use, multi-agent systems, evaluation and guardrails, AI system design, and cost/latency/safety — with hands-on labs in Python.

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Start: LLM Fundamentals & Prompting →
📊 Assess your AI Engineering level to see exactly which modules to focus on.Assess →
1
LLM Fundamentals & Prompting
Master the levers behind every LLM call — tokens, context windows, temperature, system prompts, and few-shot examples — to steer output without touching a single weight.
Free
2
Retrieval-Augmented Generation
Ground answers in your own documents with chunking, embeddings, similarity search, and re-ranking — killing hallucination and staleness without the cost of fine-tuning.
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Chapter review
Chapter 1 review — Foundations: prompting & RAG
Lab: Build a grounded Q&A bot · 10-question check
🔒 Standard
3
Agents & Tool Use
Turn a model into an agent that reasons, acts, and iterates — wiring up JSON-schema tools, executing calls in your own code, and setting stop conditions that prevent runaway loops.
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4
Multi-Agent Systems
Scale beyond a single agent with orchestrator-worker patterns and isolated contexts — and learn the coordination, latency, and token costs that tell you when one agent wins.
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Chapter review
Chapter 2 review — Agents & multi-agent systems
Lab: From one agent to a team · 10-question check
🔒 Standard
5
Evaluation & Guardrails
Ship non-deterministic models with confidence using golden-set evals, LLM-as-judge scoring, and guardrails that block prompt injection, PII, and off-schema output before it ever returns.
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6
AI System Design
Make the architectural calls that ship real LLM features — RAG versus fine-tuning, structured outputs, prompt caching, and model routing with fallbacks — weighing every tradeoff.
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7
Cost, Latency & Safety
Meet production's three-way test — cost, latency, and safety — with streaming, caching, max_tokens caps, least-privilege tools, and grounded answers that know when to abstain.
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Chapter review
Chapter 3 review — Production: evals, design, cost & safety
Lab: Productionize the assistant · 10-question check
🔒 Standard
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Final exam
AI Engineering Essentials — comprehensive capstone
Hands-on capstone + course-wide exam · scored to a role level
🔒 Professional