This resonates. I built an AI career consultant (CareerCraft AI) with exactly this philosophy — describe what the user needs to accomplish, not how to implement it.

The spec was essentially: "A user shares their professional background and a target job posting. The AI has a conversation to understand their experience, then generates a tailored resume and cover letter as downloadable PDFs. It remembers their profile within the session so they can generate documents for multiple roles."

That's it. No formal grammar, no structured templates for the conversation flow. The LLM handles the "how" — when to ask clarifying questions, how to reframe experience for different roles, what to emphasize based on the job description.

The interesting finding: natural language specs work remarkably well for applications where the output is also natural language (documents, advice, analysis). The formal spec approach makes more sense when the output needs to be deterministic code.

Try it if you're curious: https://super.myninja.ai/apps/6de082c7-a05f-4fc5-a7d3-ab56cc...

> I want to be able to run the same spec again and see the LLM add a feature that I never expected (and wasn't in the last version run from the same spec) or modify tactics to accomplish user goals based on changes in technology or availability of new standards/vendors.

Be careful what you wish for. This sounds great in theory but in practice it will probably mean a migration path for the users (UX changes, small details changed, cost dynamics and a large etc.)

I tried this recently with what I thought was a simple layout, but probably uncommon for CSS. It took an extremely long back and forth to nail it down. It seemingly had no understanding how to achieve what I wanted. A couple sentences would have been clear to a person. Sometimes LLMs are fantastic and sometimes they are brain dead.