Learn how to use AI
A structured path from "what is an LLM" through verifying output, using AI at work, and building with it. Work through it module by module — your progress is tracked as you go.
Your progress
90 guides across 4 tracks. Pick any module to begin — progress saves on this device.
Working with AI
0/60 done · 8 modules
Start here
The foundations that make every later guide easier. Read this pillar first if you are new to AI.
Verify and trust
What AI gets wrong, how to check its output, and what is safe to share at work.
How AI works
The mechanism under the hood — tokens, embeddings, transformers, context windows, agents. For the curious operator who wants to understand, not just use.
Use AI for work
Concrete task-based playbooks — email, meetings, research, weekly reports, second-brain capture.
Build with AI
Prompting your way to working software. Vibe coding, code review, working on existing codebases.
Ship and own your stack
Moving an app off Lovable, Replit, Base44, or Emergent onto hosting, a database, and a repo you control, without rebuilding it from scratch.
AI by role
Role-specific playbooks for sales, marketing, founders, HR, PM, engineering, design, ops.
Choose the right AI
Which model for which job, when a chat window is enough vs when to reach for a coding agent, and when the paid tier actually pays off.
Building & shipping
0/6 done · 3 modules
Prompt Engineering
0/12 done · 3 modules
Prompt fundamentals
The patterns that hold across every model — clarity, structure, examples, and the five moves that work every time.
Prompting techniques
Beyond the basics — chain-of-thought, prompt chaining, role assignment, and format control for predictable, repeatable results.
Building with prompts
For anyone embedding prompts in a product or workflow — system prompts, iteration discipline, injection defense, and cross-model porting.
Stacks & systems
0/9 done · 4 modules
Picking your stack
Build vs buy, what to roll yourself, what to outsource to a SaaS.
Integration patterns
Connecting tools without writing a backend. Webhooks, polling, native integrations.
Automation
Zapier vs Make vs n8n vs scripts. Personal automations worth the setup.
Running AI tools
Getting the tools actually running on your machine — installing CLI coding agents, what MCP is, and connecting AI to your own systems.
Sequenced routes through the library
Three multi-guide paths shaped by role and goal: operator, engineer, PM. An ordered reading list with notes — work through it at your own pace.
Recently updated
Webhooks vs polling: when each one wins
Webhooks push the moment something happens and fail silently when your endpoint is down. Polling is slower and far easier to debug and replay. How to pick, and how to make either one reliable.
Connecting tools without writing a backend
Webhooks, native integrations, Zapier as glue. When you can avoid a backend entirely, and when the lack of one starts costing you.
Prompting an AI app builder for a real backend: database, auth, and payments
App builders default to a front end with mock data unless the prompt names the backend. What to specify, in what order, and where prompting stops.
RAG vs an indexed knowledge base: what is the difference?
A knowledge base is the content; RAG is a technique for querying it. The real choice underneath is the retrieval method: vector search finds meaning, a keyword index finds exact strings. When each wins, and why most systems run both.