AI can help write content faster, but churning out AI-generated content isn’t enough for technical audiences who value quality.
To scale content without sacrificing quality, you need to use AI in the three stages of content creation:
- Research
- Writing
- Editing
Using AI for Research
Research and content briefs are the foundation of quality content, but they’re also time-consuming. Before you create a valuable guide or documentation update, you need to identify what your audience needs to know, understand what’s already been written, and gather information from multiple credible sources. This research phase can take hours.
Now, with AI research tools such as ChatGPT, Perplexity, or Claude, this timeline can be reduced to minutes. Rather than manually reviewing competitor documentation, blog posts, and technical resources, AI extracts key insights and returns them with source citations for verification.
You get a solid foundation of relevant information without spending hours hunting for it.
Creating Content Using AI + Templates
Creating content from scratch every time is slow and mentally exhausting.
Each piece requires you to make the same decisions repeatedly, what structure works, how much detail to include, and what tone to use, adding cognitive overhead that slows production and creates inconsistency.
Using templates for different content types solves this. By defining your content baseline, you can create more consistent, high-quality content using AI.
You can use LLM chat apps like ChatGPT or Claude to analyze your best-performing content or industry examples, identifying the patterns that make them effective, and turn them into templates. Include the templates in your content-generation prompts to produce content that adheres to the proven structure and quality standards you’ve defined.
This ensures AI-generated content matches your quality standards from the first draft, reducing the need for extensive rewrites.
Using AI to Review Content
Most people use naive techniques when reviewing content with AI.
They paste content into ChatGPT with vague prompts such as “review this” or “make this better,” without specifying the standards or criteria to evaluate against. This results in inconsistent feedback across reviews, making it unreliable as a quality gate.
To get more reliable reviews, create a review checklist based on your style guide and include it in your review prompt. A review checklist breaks down your quality standards into actionable items that the LLM can use to identify issues and suggest fixes to them.
Beyond manually pasting review prompts, you can automate the review process using prose linters like VectorLint in GitHub Actions. This ensures consistent evaluations and style enforcement across your team, with every piece of content automatically reviewed against your style guide before reaching human reviewers.
Catching style and quality issues at multiple stages of your workflow reduces review cycles and enables faster content delivery.
Start Small, Scale Gradually
You don’t need to implement all three strategies at once to see results. Start with research, then add templates, and finally automate the review process.
Use Perplexity or Claude to generate research reports that you can feed directly into your content generation AI. This ensures the AI only cites information from your research, making the output more accurate. Verify key facts and technical details before using the research in production content.
You can start with publicly available templates or use an LLM tool to generate templates from proven content, then include them in your content-generation prompts to produce cleaner drafts.
Start with ChatGPT and a review checklist based on your style guide to speed up your review process. If you use a Docs as Code workflow, implement automated reviews in GitHub Actions using prose linters such as Vale, Markdownlint, and VectorLint.
AI-assisted research, template-driven content, and automated review workflows are all you need to scale your content strategy.