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I Built SpecDD Because AI Kept Forgetting What We Were Building - and Between the Two of Us, We Couldn't Spec-ify a Thing
A creator’s account of specification-driven development, why context is the real bottleneck, and what happens when you stop treating AI like a search engine and start treating it like a tool that can actually read the manual.
There is a moment that every developer working with AI agents eventually hits. You have been building something for a while - a billing module, a new API, a data pipeline - and then you ask the agent to implement the next logical piece. It comes back with something that contradicts what you instructed on three sessions ago. A dependency appears that you explicitly said was forbidden. A boundary gets crossed. A pattern gets abandoned in favor of something the model apparently prefers from its training data. The code is often technically correct, but it is wrong for your project.
I hit that moment a lot. It annoyed me greatly. And then I started thinking hard about why it kept happening.
The mainstream response to this frustration, as best I could observe it, was to reach for more. More tokens. More retries. More elaborate prompting strategies. Entire products were built around the idea of throwing larger and larger amounts of context at the problem and hoping the model would find the relevant signal buried somewhere inside it. The industry collectively decided that if the thing was not working well enough, the answer was to feed it more. Claw back context from wherever you could find it. Stuff the window. Spend the tokens. Ship the wrapper.
I found that unsatisfying in the way that only a real engineer can find a workaround unsatisfying. Not because it never worked, but because it was treating the symptom rather than the cause, and I have never been particularly good at leaving causes alone once I have identified one. If something is not working the way I want it to, my instinct is not to compensate around it. My instinct is to understand it well enough to make it work properly. That is, I would argue, what engineering actually is.
So instead of spending more tokens, I started asking a different question: what would it take to give an AI agent exactly the right context, in exactly the right place, in a form it could actually use reliably? Not approximately right. Not probably enough. Exactly right. And that question, pursued far enough, is what became SpecDD.
Will AI Make Developers Obsolete? Wrong Question.
Before getting into the diagnosis, though, I want to address something I hear constantly in conversations about AI-assisted development, because it shapes how people think about the problem and whether they believe a solution like SpecDD is even worth pursuing.
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