Ways to make the output trustworthy

When AI produces something you are going to put live, trustworthy means you can rely on it without re-checking every line by hand. That trust is not automatic. You build it by being explicit about what good looks like, bringing in fresh eyes, and verifying the parts that carry real risk. This page is about the moves that turn a plausible-looking output into one you can put your name on.

Ask for a shape, not prose

When you need data back from AI in a fixed form, the reliable move is to hand it a schema, the defined shape of the thing you want, and have it fill that in, rather than describing the JSON you want in a prompt and hoping. Ask for a shape, not prose →

Examples keep extraction honest

The other way to a trustworthy result is by example. Examples keep extraction honest →

Validate and retry

Structured output gives you the shape; validating is checking the values are actually right, and when they are not, feeding back the specific failure so the AI corrects it rather than starting cold. Validate and retry →

Review with fresh eyes

The maker is the worst reviewer of its own work. Review with fresh eyes →

Guard against false positives

A check is only trustworthy if you act on what it says. Guard against false positives →

Two more trustworthy moves already live on the getting a sharp answer page: setting checkable criteria so quality is not a matter of opinion, and the Sharpen loop for tightening a draft until two people would decide the same way.

Part of Working with AI. The soft skills behind it: The soft skills of working with AI.

Why Claude. When this page carries tool-specific notes they use Claude Code, because it is one of the most widely used AI tools for building software. It is also what we build with day to day. The moves themselves are general and carry across to other capable AI tools.