Using AI to Review Blog Posts and Knowing When to Stop

After two years of publishing articles on this website, I noticed my writing style had gradually drifted. As a programmer, the inconsistency bothered me, so I decided to use AI to review my previous posts and help establish a more consistent house style.

There was another reason for doing this. Maintaining a technology-focused website means keeping up with rapidly changing software and platforms. I wanted to use the review process not only to make the writing more consistent, but also to identify technical information, code examples, and explanations that needed updating.

This took much longer than I expected.

I try to make my posts reasonably comprehensive. I do not like searching for a solution and finding articles that half-explain something or miss the key technical detail needed to make the solution work. I established a house style and review rules, then used AI to identify gaps and maintain a consistent editorial approach across the site.

It also created an endless editing loop.

AI reviews the content and provides suggestions. I evaluate those suggestions, decide what to address, and repeat the process. The cycle could continue indefinitely because AI can almost always find something else to change or polish.

This was particularly noticeable with code. AI identified conditions, edge cases, and common troubleshooting scenarios that I had not considered when I originally wrote some articles. Those suggestions helped me expand several articles in genuinely useful ways and better support my goal of not publishing partial solutions.

At the same time, AI identified holes in code that I have used for years without issue. That raised an interesting question: if I have used something for years without encountering a problem, how significant is the theoretical problem AI has identified?

A technically possible failure case is not necessarily a practically important one. There is a difference between identifying a potential failure, demonstrating the failure, and establishing whether the risk justifies changing working code. An AI reviewer can be very good at finding possibilities. Determining whether those possibilities matter still requires human judgment.

The exercise also reminded me that programming experience does not eliminate opportunities to learn and grow. Some suggestions resulted in better articles and better code examples. Others were technically valid but did not justify changing the existing content.

That distinction applies to writing as well. I suppose the same is true of anything creative. When is a painting done? When is photo editing done? When is coding done? There is always another feature or tweak to make. At some point, however, the work has to be considered finished.

With software, that point is usually obvious. The code needs to be frozen and deployed. If every possible improvement had to be made before deployment, nothing would ever ship.

Writing has the same problem, although it is easier to overlook because I have no editor or deadline telling me when to stop. An article can always be reworded, reorganized, expanded, or clarified. An AI reviewer makes this even more apparent because it can continue generating plausible suggestions long after an article is already useful.

After going through this process, I do not think I would do it again.

AI was still useful. It found real inconsistencies, identified technical gaps, and suggested improvements I would not necessarily have found on my own. The question is whether everything needs to be perfectly consistent.

I am a human, and my writing, especially on a blog, should not be perfectly consistent over time. People change. Their interests change. Their experience changes. Their writing changes. Some variation between articles written years apart reflects those changes rather than a problem that needs correction.

There is also a difference between consistency of mechanics and consistency of voice. I want terminology, formatting, capitalization, and technical standards to remain reasonably consistent. I do not necessarily want every article to sound as though it was written on the same day by the same version of me.

AI changes as well. The suggestions AI generates today will likely differ from its suggestions a few months from now. If I continually use the latest AI recommendations to make older articles conform to its current preferences, I could end up repeatedly rewriting perfectly acceptable content simply because the reviewer changed.

In hindsight, I would separate the things that benefit from consistency from the things that do not. Technical accuracy is worth reviewing. Outdated code examples are worth refreshing. Broken instructions and missing edge cases are worth addressing when they materially affect an article’s usefulness. Terminology and formatting should remain reasonably consistent. The evolution of my writing style does not need the same treatment.

For future reviews, I would likely limit AI’s role primarily to technical and code refreshes, along with identifying substantive gaps that affect an article’s usefulness. I would be much less interested in repeatedly polishing wording simply because another version could be made slightly more consistent.

The goal should not be to make every article perfect. The goal should be to make each article useful, accurate, and finished enough to publish.