The Skeleton of the Method
The author's process has a few fixed pillars: before starting, talk it out with the model to grind the requirement into a clear spec, and have it restate its understanding before beginning; slice the task into small blocks, each with a verifiable completion criterion, and never let the model write a big chunk in one go; test-first or at least in parallel, so the model's output always passes through the test gate; and manage context diligently, periodically resetting long sessions and distilling conclusions into documents. The spirit of the whole method is to treat the model as a "very capable collaborator who needs to be managed"—not skipping a single management action.
Common Ground Is Emerging
Put a few of the community's well-regarded workflow accounts side by side, and you'll find they converge by different roads: the productive people are all doing spec-writing, small steps, and strong verification, and the horror stories all come from doing the opposite. This shows that best practices for AI programming are converging into shape, and it increasingly looks like "the discipline of agile development plus the skepticism of code review," rather than any prompt magic. For beginners, articles like this are worth reading before any prompt cheat sheet—the method transfers far better than incantations do. Its other value is demystification: the experts use the same model you do; the difference is process.
via: Hacker News