Why AI backend code breaks when specs are vague (and what I’m building to fix it)
Source: Dev.to
The Problem with Vague Backend Specs
AI tools like Cursor and ChatGPT are incredibly useful, but when building backend features I kept running into the same problem: vague requirements cause the AI to start guessing. This usually leads to:
- Incorrect business logic
- APIs that don’t match real product needs
- Authentication and data rules breaking
- Rewriting the same code multiple times
Why the Issue Isn’t the AI
The root cause is unclear backend specifications, not the AI itself.
Instead of jumping straight into code generation, I began defining the following first:
- Data models and relationships
- API contracts
- Authentication and authorization rules
- Error handling and edge cases
My Solution: Define Specs Before Coding
By establishing clear specifications up front, the AI can generate accurate, implementation‑ready code. This approach dramatically reduces guesswork and rework.
Introducing Onvyo
Onvyo is an AI‑powered developer tool that generates end‑to‑end, implementation‑ready backend specifications before any code is written or generated. It uses a structured interview flow (instead of free‑text prompting) and outputs a detailed backend spec in Markdown that you can paste directly into tools like Cursor or ChatGPT.
Goal: Give AI clear, deterministic instructions so it stops guessing and starts generating correct backend code.
Onvyo is now live and still early: I’m building this in public and actively collecting feedback.
Call to Action
If you’ve struggled with AI‑generated backend code due to unclear specs, I’d love to hear how you’re handling it today.