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AI Fundamentals
How language models work, writing prompts and project rules, curating context, agents and tools, verifying AI output, and using AI safely.
6 modules · 31 lessons. Readable online with no signup, or offline as an ebook via the free newsletter.
View course (opens in new tab)Curriculum
- 1
Understand AI models
Build a useful mental model of generation, tokens, capabilities, and limits.
- 01 What AI is useful for
- 02 What a language model does
- 03 Tokens and the context window
- 04 Capabilities and limits
- 05 AI glossary
- 06 Check your understanding: AI models
- 2
Choose and access a model
Compare models by task, speed, cost, access method, and privacy needs.
- 07 Compare models by the task
- 08 Model names will change
- 09 Subscription or API key
- 10 Cloud or local models
- 11 Check your understanding: choosing a model
- 3
Prompts, rules, and context
Give AI a clear task, durable instructions, and only the context it needs.
- 12 Prompts, rules, and context
- 13 Write a useful prompt
- 14 Use project rules
- 15 Curate the context
- 16 Check your understanding: prompts and context
- 4
Agents and tools
Understand the agent loop, tool calls, permissions, skills, and MCP.
- 17 Chatbots and agents
- 18 The agent loop
- 19 Tools and permissions
- 20 Skills and MCP
- 21 Check your understanding: agents and tools
- 5
Verify the work
Work in small steps and use inspection, tests, and evidence as feedback.
- 22 Work in small steps
- 23 Inspect before changing
- 24 Use tests as feedback
- 25 Verify the result
- 26 Check your understanding: verification
- 6
Use AI safely
Handle uncertainty, private data, secrets, permissions, and generated code responsibly.
- 27 Hallucinations and uncertainty
- 28 Protect data and secrets
- 29 Review generated code for security
- 30 Complete your first AI workflow
- 31 Check your understanding: safe AI use
Author
Flavio CopesMore from Flavio Copes
AI Agent Skills
Turn a repeated workflow into an Agent Skill, design its discovery and activation, write SKILL.md, bundle scripts, references, and assets, then evaluate, secure, and ship it.
Build a Language Model on Your Mac
Train a small language model with Language Model Builder on a Mac. Covers tokens and embeddings, designing the run, pre-training and reading loss, fine-tuning with SFT and DPO, and comparing checkpoints in a lab report.
Build with MCP
Build a TypeScript MCP server with tools, resources, and prompts, move it to remote HTTP with authorization and security boundaries, then deploy and review it.
Local AI Models
Choose an open-weight model that fits your hardware, run it locally with Ollama and Node.js, build a private AI feature with structured output and fallbacks, and operate local AI responsibly.
MCP
What MCP solves, how hosts, clients, servers, tools, resources, and prompts fit together, and how to connect, configure, test, and troubleshoot an existing server you trust.
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