How do you know your AI project is ready for the next milestone?

How do you know your AI project is ready for the next milestone?

A milestone is ready when the agreed behavior works and you have evidence to show it. The existence of generated code or a polished screen does not establish that the user can complete the task.

Set acceptance checks before implementation

For a script-generation milestone, you might require that a valid brief produces an editable draft, empty input receives a clear error, and saved edits remain available after reload. These checks describe observable outcomes instead of internal implementation choices.

Keep the checks aligned with the milestone. A script-review feature does not need to prove that a later social-publishing integration works, but it should reliably provide the reviewed text that the next stage will consume.

Evaluate workflow and output separately

Use a small, representative set of inputs. Include a straightforward brief, a brief with important constraints, and an incomplete input. Confirm that the application handles each appropriately.

Then assess generated content. Does the script follow the brief? Can the intended audience understand it? Are factual claims checked? Generative outputs can vary, so avoid requiring identical wording when the real requirement is accuracy or usefulness.

Check recovery and persistence

Reload the page, reopen a saved project, and exercise a relevant failure path. Confirm that the interface reflects the actual saved state and that users can find completed results. For private projects, verify that another user cannot retrieve them.

Record the result of each check and any known limitation. If a requirement fails, correct it or explicitly revise the scope before declaring the milestone complete. This creates a dependable starting point for the next stage and prevents later features from hiding unfinished work underneath them.