AI Literacy for EMs

🚧 Expanding

You don’t need to train a model to lead engineers who use them — but you do need a working mental model, the kind that lets you smell a bad claim, scope a feature realistically, and ask the question that cuts through a demo. AI literacy for a manager isn’t about prompt tricks; it’s about understanding what these systems are good at, where they quietly fail, and what that means for how your team builds. It’s the foundation under everything else in this section — whether your engineers are leaning on assistants in their everyday workflow or putting a model at the core of the product. This page is a stub, but the goal is to get you to “dangerous enough to be useful” without drowning you in research papers.

What this will eventually cover:

What an LLM actually is (and isn’t) — context windows, tokens, why it confidently makes things up
The vocabulary you’ll keep hearing: prompting, RAG, agents, tool use, fine-tuning, evals
How to reason about cost, latency, and capability when scoping AI-touched work
Reading a capability claim or a benchmark without getting fooled

💡

The literacy that’s paid off most for me isn’t technical — it’s knowing the failure modes. A model will give you a fluent, plausible, completely wrong answer with total confidence. Once you internalize that, you stop asking “is the AI right?” and start asking “how would we know if it weren’t?” — which is the question that actually protects your product.

📚 Go Deeper

Books

Co-Intelligence — Ethan MollickThe best non-hype, non-doom orientation to what these tools are and how to actually work alongside them. Start here.

Courses

DeepLearning.AI — short coursesFree, hands-on, no-fluff courses on LLMs, prompting, and agents. The fastest way to turn vague intuition into real understanding.

Tools

Anthropic documentationRead how a frontier model actually works — context windows, tool use, prompting. Concepts here carry across every provider.

Last updated on June 25, 2026

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