With LLMs now capable of creating and reviewing content at scale, your Docs as Code workflow is incomplete without an AI prose linter.

Although traditional prose linters can catch many errors, their syntactic approach means they can’t catch errors that require contextual judgment.

To solve this problem, many teams use LLM-powered apps like ChatGPT or Claude. However, this remains outside the team’s shared automated testing workflow, resulting in inconsistent quality.

These apps aren’t tuned for consistent evaluations, and different team members use different prompts and processes. Even with a shared prompt library, you’re still relying on each contributor to use it correctly.

An AI prose linter solves this by providing AI reviews and suggestions in your Docs-as-Code workflow. You can achieve reliable automated quality checks by setting the LLM to low temperatures, using structured prompts, and configuring severity levels.

Making AI Prose Linters Reliable With Severity Levels

AI prose linters inherit the non-determinism of their underlying technology, which means they will occasionally generate false positives.

Because the whole point of a CI pipeline is to deliver reliable builds, this is a bad recipe for your pipeline. The solution is to configure them as non-blocking checks that highlight potential issues and suggest fixes without failing your build.

Just like traditional prose linters aren’t perfect, AI prose linters don’t need to be either.

Even if you get 50% accuracy on quality flags, you’d be saving half the time you’d otherwise spend hunting for them yourself.

With that out of the way, here are four reasons you should adopt an AI prose linter in your Docs as Code workflow.

1. It Reduces Time Spent on Reviews

AI prose linters reduce the time spent on manual content reviews by catching contextual issues that typically require human judgment.

While traditional prose linters can catch terminology and consistency issues, the bulk of review time is typically spent on editorial feedback. This involves identifying issues that require contextual judgment, such as whether there is repetition of concepts across sections or if content directly answers the reader’s question.

By codifying these editorial standards into AI prose linter instructions, you can catch these issues locally or in the CI pipeline and get suggested fixes. This reduces the mental load on reviewers and saves time.

2. It Enables Broader Team Contribution

AI prose linting enables developers, engineers, and product managers to contribute high-quality documentation by providing them with immediate, expert-level editorial feedback as they write.

Technical writers are often stretched, with some teams operating at a 200:1 developer-to-writer ratio. To get documentation up to date promptly, non-writers often need to contribute. While you can save a lot of time with traditional linters catching typos and broken links, you can make contributing even easier by using AI prose linting.

Not only does it broaden the scope of issues you catch, but it also helps contributors learn the reason behind the flags and provides them with suggestions to fix them, making them more confident in their contributions.

3. It Lowers the Barrier to Docs as Code

Teams that don’t have a dedicated documentation engineer often refrain from adopting a Docs as Code workflow because of its maintenance overhead. It often requires an ongoing effort to create and maintain rules as the team creates more content.

While traditional linters often have preset style rules that you can start with, you’ll still need to maintain them to deal with false positives that block merges, or to catch new issues that come up.

AI prose linters solve this problem by using natural language instructions to define rules. This enables you to catch a wide range of issues with fewer instructions, reducing the maintenance overhead.

For instance, if you wanted to catch hedging language using Vale, you’d need to write a regular expression covering as many variations as you can think of, such as appears to, seems like, mostly, I think, sort of, etc.

With an AI prose linter, you can simply write:

Check for any phrase that connotes uncertainty or lack of confidence (for example, “appears to”, “seems like”).

And it can catch variations you never thought to list.

The trade-off is that natural language tends to leave room for edge cases, and so without precise instruction, you can get false positives. However, the cost of maintaining a wide library of rules or trying to envisage every edge case far outweighs the cost of filtering out false positives.

4. It Accelerates Productivity For Solo Writers

To achieve high-quality, error-free content, solo writers still have to review their own work. However, the biggest hurdle isn’t a lack of skill; it’s the human factor. When you’re the only person writing and editing thousands of lines of documentation, you lose the “fresh eyes” benefit that teams take for granted.

After the fifth hour of editing a technical guide, fatigue sets in, making it easy to miss quality issues. An AI prose linter serves as a peer reviewer, turning the review process into simple “yes” or “no” decisions.

The AI highlights the issues, and you decide whether they’re valid quality issues or not. This is less mentally taxing and faster than if you had to find the issues yourself.

Knowing you have an automated editorial pass gives you confidence, allowing you to focus on providing value rather than worrying if you’ve missed a subtle stylistic error.

Using VectorLint, an Open Source AI Prose Linter

VectorLint is the first command-line AI prose linting tool.

We built it to integrate with existing Docs-as-Code tooling, giving your team a shared, automated way to catch contextual quality issues alongside your traditional linters.

You can define rules in Markdown to check for SEO optimization, AI-generated patterns, technical accuracy, or tone consistency, practically any quality standard you can describe objectively.

Like Vale or other linters you already use, VectorLint runs in your terminal and CI/CD pipeline as part of your standard testing workflow.

Check it out on Github