Curriculum
All 41 units. Filter by track or career path.
Every module ships with a teacher guide, student worksheet, scenarios packet, quiz, answer key, and an in-browser presenter. Free preview available on the orientation modules of every track.
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Showing 6 of 41 modules for AI Engineer. Clear filters
AI Fluency, Unit 2: How LLMs Actually Work
Crack open the box. Tokens, embeddings, attention, training vs. inference, context windows, and why the model hallucinates - explained at a level a 10th grader can teach back.
180 min · foundational
View module ->AI Fluency, Unit 3: Prompt Engineering Fundamentals
The single highest-leverage skill in AI work. Students learn the C.R.I.S.P. prompt frame, system vs. user roles, zero-shot vs. few-shot, format control, and the iteration loop that separates 'AI is mid' from 'AI is incredible.'
180 min · foundational
View module ->AI Fluency, Unit 5: The Big LLMs - Comparing Frontier Models
Hands-on tour of the frontier: GPT, Claude, Gemini, and the open-weight challengers. Students compare strengths, context windows, pricing, safety behavior, and learn to pick the right model for the job instead of defaulting to whatever app they opened first.
180 min · intermediate
View module ->AI Fluency, Unit 6: Local & Open-Source LLMs - Run Your Own AI
Stop being a user. Become an operator. Install Ollama or LM Studio, download Llama / Mistral / Qwen / Phi, and run a real LLM on a laptop with the wifi turned off. The unit that changes how students think about AI.
240 min · intermediate
View module ->AI Fluency, Unit 7: Retrieval, Tools, and Agents
From chatbot to product. RAG (retrieval-augmented generation) so the model has actual sources, function calling and MCP so the model can use tools, and agents that loop. Plus the honest conversation about when agents go off the rails.
180 min · intermediate
View module ->AI Fluency, Unit 10: Capstone - Build a Useful AI Tool
Put it all together. Each student ships a working AI tool that solves a real problem in their life: a local-LLM study buddy, a prompt-library product, a RAG over their notes, or an image-gen workflow. Disclose, verify, contribute - graded for real.
240 min · applied
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