People Use AI
Software Engineering

Using AI to support testing and handoff documentation

The author describes using AI to write tests and generate development summary documents that capture what was built, the current data model, unfinished work, known issues, and assumptions for the next coding cycle.

Why the human is still essential here

The developer verifies test coverage, confirms that features work correctly, and uses judgment to decide what context and documentation should carry forward.

How people use this

Unit test generation

AI drafts unit tests for newly written functions and components so developers can quickly expand baseline coverage before review.

GitHub Copilot / Qodo

Regression test drafting

AI analyzes recent code changes and suggests additional tests for likely edge cases and failure paths that could break existing behavior.

Qodo / GitHub Copilot

Development handoff summary

AI compiles a concise handoff note describing completed work, current architecture, open issues, and next steps for the next coding session or teammate.

Claude / ChatGPT

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Blog

Writing Custom Applications Using AI

Whether we like it or not, AI is here to stay. Personally, I am tired of hearing about it and how people "vibe coded" something. That being said, that is pretty much what this post is about. Just to clarify, I am using AI to refer to Large Language Models also known as LLMs.

I was asked to have a meeting to talk to other developers about how I use AI tools to write code. I have over time found it to be a major time saver using AI tools by letting it focus on the coding and let me focus on making sure the product is what the users are looking for.

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KW
Kevin WilliamsBusiness Intelligence Practice Lead at Software Design Partners
Mar 14, 2026