Most AI coding agents fail not because they lack coding ability, but because they lack context. After months of hitting a wall with Cursor and Bolt, I realized a 1-sentence prompt is the fastest way to get trapped in a "bug-fixing loop."
The missing link isn't better AI—it’s Technical Grammar.
I built https://ideaforge.chat to shift the workflow from "prompting" to "specification forging."
What’s different about this approach?
Adaptive Interviewing: Instead of a static template, the AI acts as a PM/Technical Co-founder that grills you on logic gaps you didn't know existed.
Forcing Technical Decisions: It won't let you start coding until you've defined things like data relationships, session persistence, and edge cases.
Machine-Readable Specs: The output is a structured Markdown document specifically formatted to maximize the performance of coding agents like Cursor or Windsurf.
I’ve moved from "broken apps on the first try" to "MVPs that actually work" by using this interview-first method.
To the engineers: Is the "Specification Gap" something you see as the primary bottleneck for non-technical partners? I’d love your feedback on whether this Socratic approach produces the kind of PRD you’d actually find useful for a high-stakes project.
Why "Garbage In, Garbage Out" is the biggest bottleneck in AI coding · HackerTrans
Most AI coding agents fail not because they lack coding ability, but because they lack context. After months of hitting a wall with Cursor and Bolt, I realized a 1-sentence prompt is the fastest way to get trapped in a "bug-fixing loop."
The missing link isn't better AI—it’s Technical Grammar.
I built https://ideaforge.chat to shift the workflow from "prompting" to "specification forging."
What’s different about this approach?
Adaptive Interviewing: Instead of a static template, the AI acts as a PM/Technical Co-founder that grills you on logic gaps you didn't know existed.
Forcing Technical Decisions: It won't let you start coding until you've defined things like data relationships, session persistence, and edge cases.
Machine-Readable Specs: The output is a structured Markdown document specifically formatted to maximize the performance of coding agents like Cursor or Windsurf.
I’ve moved from "broken apps on the first try" to "MVPs that actually work" by using this interview-first method.
To the engineers: Is the "Specification Gap" something you see as the primary bottleneck for non-technical partners? I’d love your feedback on whether this Socratic approach produces the kind of PRD you’d actually find useful for a high-stakes project.