Lovable — getting started
Documentation for one of the AI-assisted application builders, aimed at people who are not developers.

What tools like Claude Code and Lovable make possible for someone who is not a developer, using a real example.
AI coding assistants have genuinely lowered the floor. Somebody who understands the department's work, but has never shipped software, can now produce a working internal tool in an afternoon. The work that suits this well is administrative: workflow that is currently a spreadsheet and three emails, equipment tracking, internal forms, dashboards, data cleanup, small automations, prototypes to show what you mean, and training tools. What has not changed is responsibility for the result. AI-generated code is not automatically secure and not automatically correct. It is confident in both cases. It will happily write something that works on your test data and fails on the real thing, or that stores a password in a way no one would choose deliberately. So treat generated code the way you would treat work from a capable but unfamiliar contractor: useful, fast, and requiring review by someone who understands what it is touching before it goes anywhere near real data.
If you read one thing on this subject, read this.
Documentation for one of the AI-assisted application builders, aimed at people who are not developers.
A framework for identifying and managing risk in AI systems.
Why it matters
Applies to using AI to build things, not only to buying AI products.
How a department sets policy for AI use.
Why it matters
If you are building with AI, the same governance questions apply to you.
Everything above was published by someone else, and is here because it is the clearest treatment of the subject we could find and verify. The Hub’s own guide to this topic is still being written. If you know a better source than the ones listed, that is worth telling us before it is.
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