Tagprocess

The 90/10 Problem

AI built most of a recent feature, and built it well. Then UAT surfaced a requirement from a client meeting that never made it into the planning docs.

This is about the last 10%: the context that never reached the AI, and the judgement calls about risk and timeline that it can't make on your behalf.

The Model in Practice

Six issues describing five phases, and I don't use them that way any more. Plan, specify and constrain have collapsed into a single conversation.

The collapse is the success condition rather than a failure. The five phases remain the fastest way to diagnose work that has gone sideways, even when they're no longer sequential steps.

Skills as a Type System for AI

Ad-hoc prompts produce inconsistent documentation because the AI doesn't know your standards. You can hand a junior engineer a style guide; with prompts there is no equivalent.

So I built one. Encoding standards as reusable skills works like a type signature — constrain the input and the output, and the shape of the work stops drifting.

Tests as Verification, Not Ceremony

When AI writes code at volume you can't read every line, and you shouldn't have to. Tests become how you verify output without parsing the implementation.

This is about why most coverage targets are theatre, and the question worth asking instead: if I read only the tests, would I know whether this code is correct?

Planning Is the Highest-Leverage Work

If you skip planning and go straight to "build this", the AI will invent a plan for you. It won't reflect your domain, your constraints, or what you actually need.

This covers which planning artefacts earn their keep — PRDs, mapping docs, statements of intent — and a short readiness checklist for deciding whether work is ready to hand over.

AI as a Teammate, Not a Tool

We estimated 232 hours for the content and meta migrations on a major editorial platform. It took 80, at the same scope and the same review rigour.

The difference came from how the work was divided: AI as a product manager before any code, then as a junior engineer afterwards, with me as tech lead throughout.

AI Reliability Over AI Hype

The pitch is speed. The reality, for a lot of engineers, is bloated PRs full of defensive code that nobody wants to review.

AI isn't unreliable; unconstrained usage is. This is about what changes when you define shapes, types and tests up front rather than hoping a better prompt will do the work for you.