Spec-driven AI coding with GitHub’s Spec Kit

You can now follow up by adding the technology stack you intend to use, in the shape of a /plan. This allows you to change elements of your stack, such as swapping out authentication methods or storage, allowing you to go from development to test to deployment. So, you could start with a basic SQLite implementation for development on your own PC, rebuild for MySQL on a test system, and then change to Azure SQL for deployment.

One important feature of Spec Kit is that the base system prompts are designed to minimize the risk of hallucinations. If a pass can’t implement a function, it will insert a [NEEDS CLARIFICATION] marker along with a question as part of the code so that the agent doesn’t make assumptions and requires specific intent. The base prompts include checking for this marker, ensuring that the system checks itself to avoid skipping over issues that have been flagged in earlier passes.

Once you have a spec and a plan, you can run the /tasks step. This (plus the constraints in the constitution) breaks up the project into a series of tasks: one for a front end, one for business logic, one for service interfaces, one for configuring and working with storage, and so on. Tasks themselves can be made up of subtasks, much like the project planning phase of the software development life cycle, as you work with project managers.