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AI Fundamentals for Developers

A crash course that demystifies modern AI for technical developers. You will not train models. You will understand every piece of jargon your AI-engineer colleagues use, and build a working RAG pipeline and agent loop by hand.

Who this is for: Senior developers, tech leads, and consultants of any stack who work with AI systems but don't build them from scratch.

Time: ~5 hours of instruction (six modules) plus ~4–5 hours of optional hands-on labs (eight labs + a capstone). Each module and lab carries its own time estimate so you can self-pace. Do the reading in one or two sittings; treat the labs as take-home practice.

Choose your path

Approach A — Concept-First (Bottom-Up)

Start from the science, build up to applications. Best if you want to understand how it works before how to use it.

Start Approach A →

Approach B — Use-Case-First (Top-Down)

Start from what AI can do for you today, then peel back the layers. Best if you want to be productive fast and deepen understanding as you go.

Start Approach B →

The two paths converge after Module 2

Approaches A and B are two different on-ramps: A starts from the science, B from the use cases. They differ only in Modules 1 and 2 — from Module 3 onward (Prompt Engineering, RAG, Agents, Ecosystem) the content is identical. Pick the entry point that suits you; both paths meet at Module 3 and run the same from there.

Both paths share the same Labs, Glossary, and Resources. When you've finished the modules, the Capstone ties everything together — build and evaluate a grounded support agent that retrieves docs and calls a tool.

What you'll be able to do afterward

  1. Explain what an LLM is, how it works, and why it hallucinates — to a non-technical stakeholder.
  2. Write and iterate on effective prompts for a specific task.
  3. Explain when to use RAG vs fine-tuning vs just prompting (and when long context or plain code beats all three).
  4. Design a simple RAG pipeline on a whiteboard.
  5. Explain what an agent is, what an agent harness owns, and when agentic systems are appropriate.
  6. Tell a standard model from a reasoning model and choose between them on cost vs. accuracy.
  7. Navigate the AI framework landscape and recommend the right tool for a use case.
  8. Identify cost, latency, reliability, and responsible-AI concerns when using LLMs in production.
  9. Write a basic eval to check an AI feature — and know why "looks right" isn't a passing grade.