Spec features with AI and hand off to coding agent
I disagree. Task: I disagree. I used to spend most of my time writing code, fixing syntax, thinking through how to structure the code, looking up documentation on how to use a library.Now I first discuss with an AI Agent or ChatGPT to write a thorough spec before handing it off to an agent to code it. I don’t read every line. Instead, I thoroughly test the outcome.Bugs that the AI agent would write, I would have also wrote. Example is unexpected data that doesn’t match expectations. Can’t fault the AI for those bugs.I also find that the AI writes more bug free code than I did. It handles cases that I wouldn’t have thought of. It used best practices more often than I did.Maybe I was a bad dev before LLMs but I find myself producing better quality applications much quicker.; tools: ChatGPT. Human role: The original post does not say how a person stayed involved.; limitations: The source does not specify technical constraints, integration costs, or failure modes for this workflow. Reliance on thorough testing assumes comprehensive test coverage, which may not catch complex subsystem interactions or subtle logic errors. Human approval is critical for spec accuracy, as AI cannot fault itself for missing requirements defined by the human..
Tools
Published on Agent Work Atlas:
Overview
I disagree.
Workflow
I disagree. I used to spend most of my time writing code, fixing syntax, thinking through how to structure the code, looking up documentation on how to use a library.Now I first discuss with an AI Agent or ChatGPT to write a thorough spec before handing it off to an agent to code it. I don’t read every line. Instead, I thoroughly test the outcome.Bugs that the AI agent would write, I would have also wrote. Example is unexpected data that doesn’t match expectations. Can’t fault the AI for those bugs.I also find that the AI writes more bug free code than I did. It handles cases that I wouldn’t have thought of. It used best practices more often than I did.Maybe I was a bad dev before LLMs but I find myself producing better quality applications much quicker.
Reported result
The author reports producing higher quality applications faster and claims AI generates fewer bugs than their previous manual efforts. However, no measured metrics or specific project outcomes are provided to substantiate these claims. The assertion that AI handles edge cases better remains anecdotal and unverified by objective data.
Human role
The original post does not say how a person stayed involved.
Limitations
The source does not specify technical constraints, integration costs, or failure modes for this workflow. Reliance on thorough testing assumes comprehensive test coverage, which may not catch complex subsystem interactions or subtle logic errors. Human approval is critical for spec accuracy, as AI cannot fault itself for missing requirements defined by the human.
Try it
Act as a senior developer using an AI-assisted workflow. I will provide a feature requirement: [replaceable input: feature description]. First, collaborate with me to draft a detailed technical specification, including edge cases and acceptance criteria. Once I approve the spec, use a coding agent to generate the implementation code. Do not ask me to review every line; instead, generate a comprehensive test suite covering the spec's requirements. Run the tests and report the results. If tests fail, iterate on the code until they pass. Finally, present the working code and test results for my functional verification. Ensure you ask clarifying questions if the initial requirement is ambiguous before writing the spec.
Source reports are not independently verified. Publication date is the catalog publication date, not the original article date.