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Series · 6 parts · 26 min

AI Practices for Engineers

  1. Part 14 min readBeginnerUp next

    How LLMs Work, and How to Ask So They Stop Guessing

    A model predicts the next token from its context window and nothing else. That explains confident wrong answers and the fixes: put the source in the window, give it an out, and write the prompt as a spec that answers five questions.

  2. Part 24 min readIntermediate

    Rules Files and a Prompt Library: Make the Repo Remember

    If you type the same thing in two chats, it belongs in the repo. What earns a line in a rules file, a filled-in AGENTS.md, and a shared prompt library whose review prompt stops hiding real bugs.

  3. Part 35 min readIntermediate

    Run Coding Agents Without Babysitting, and Reset Them When They Loop

    A brief written like a ticket, a check the agent can run, a plan you approve before code, and fences set in configuration. When it goes wrong, name the pattern and change what it sees.

  4. Part 45 min readIntermediate

    AI-Generated Code: Checks That Can Fail, and the Calls That Stay Yours

    AI-generated code fails in ways that look fine, and people rate it higher than it deserves. How to prove a test can fail by breaking the code, what to read in a diff, and which decisions stay with people.

  5. Part 54 min readIntermediate

    Prompt Injection and the AI Bill: Give the Agent Less to See

    One line in an issue can make an agent send your secrets out, and a long session bills the same conversation many times. Break one leg of the lethal trifecta, make secrets unreadable, and clear context between tasks.

  6. Part 64 min readAdvanced

    Turn Repeated Fixes into Skills, Then into a Team Practice

    A correction typed into every session should become something that runs without you. A skill for API tests with a small eval and five pillars, a one-page team policy measured against a baseline, and four levels to place yourself.