Best reads for PMs & Designers

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TL;DR

You will not fix a customer complaint about AI output by editing the prompt alone.
One beta user opened an opportunity tree the AI built and found too many ungrouped cards at the top.
A prompt change that improved card wording made the AI group cards less, so each error type needed its own check.
You get a clean tree when the AI checks its own output after each step and fixes what it finds.

Your move as a PM

For your AI feature spec: write one automated check per error type, so fixing one error shows when it breaks another.

14 minProduct Talk

TL;DR

The screen around a wrong AI answer decides how much harm it does, not the user's prompt skill.
Car makers once blamed every crash on the driver, until safety researchers padded the dashboard instead.
Telling users to prompt better is the same excuse, and you do not control whether the model is wrong.
So test with prompts where the model is wrong and count how many bad answers reach a real action.

Your move as a PM

For your spec: state what the product does when the model is wrong, not only how often it is right.

14 minPatrick Neeman

TL;DR

AI features ship faster than the app structure around them, so each invents its own words and error states.
Picture a house that grew for 38 years with no plan: staircases climb into ceilings, doors open onto walls.
The fix is a repair, not a rebuild: list the things your product holds and the actions each allows.
You build each feature from named patterns and limit it to the screen the user is on.

Your move as a PM

For your spec: list the things your product holds and the actions each allows before you list a single AI feature.

11 minPatrick Neeman

TL;DR

An AI agent with no fixed role spends tokens every session proving it is safe to act.
Give it a fixed role instead, with its limits written down, and never blame it for a mistake.
The writer's own agents collected so many safety rules that no work was allowed, so he cut them to 14.
You get a test for every agent you ship: it must know its role and limits before acting.

Your move as a PM

For your agent spec: write down what the agent may touch and what happens after a mistake, before any prompt.

14 minSteve Yegge

TL;DR

A product team gets more from AI when it shares a folder of context files and prompts, not private setups.
One PM's saved prompt reads Linear, GitHub and a strategy file at sprint end and drafts her status update.
Her team grades a coding agent on finishing a real task with only the docs and an API key.
You get four rules for the folder, like sharing a prompt only once people use it often.

Your move as a PM

For your launch checklist: add one test where a coding agent finishes a task with only your docs and an API key.

8 minProduct Growth

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