Right now, most AI coding workflows still look like: prompt - generate - fix - repeat It works, but as soon as projects get bigger, things start breaking down context gets lost, code becomes inconsistent, and debugging gets messy and most importantly we are out of tokens I’ve been experimenting with something different: spec-driven development. Instead of prompting directly, you first define: what you’re building expected behavior inputs / outputs constraints and edge cases Then let the AI implement based on that. It sounds simple, but the impact is pretty big: outputs are more consistent fewer random architectural decisions easier debugging (spec = source of truth) I’ve even seen tools starting to explore this idea further (things like tracking how AI applies specs across a codebase, e.g., Traycer), which makes the workflow feel more like managing an agent than prompting a tool, acting as an orchestrator . Feels like we’re moving from vibe coding to structured AI development Curious if others think spec-driven workflows are the next step, or if prompting will stay dominant. submitted by /u/StatusPhilosopher258
Originally posted by u/StatusPhilosopher258 on r/ArtificialInteligence
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