4 Content Prerequisites for AI Tools

While optimized technical documentation has always been an essential part of a positive product experience, it takes on a new layer of importance with AI technologies. Companies agree on the high potential and benefits of AI yet struggle to launch their own AI projects or see the results they desire. 

So what’s the problem?

In 2023, Segment’s growth report stated that 71% of businesses agreed that AI is only as good as the data put into it, and now in 2025, Shelf reports that 92% of leaders admit that unstructured data issues had an impact on their Generative AI initiatives. So before rushing into new AI projects, companies must slow down and prioritize content optimizations. This will provide the foundation for AI projects that generate reliable, accurate data.

Read on to discover the four essential content prerequisites to ensure a successful AI implementation.

1. Write Structured Content

The first step in preparing your existing documentation for AI is to add structure to content. Structured content means that text is broken down into individual topics. Each topic could be a few sentences or paragraphs focused on a common subject.

Content teams use writing standards like DITA and S1000D as well as structured authoring tools like CCMSs to write structured content. Writing standards and CCMSs help authors go from lengthy Word documents to organized content arranged into precise topics.

When breaking content down into topics, each topic must align with the main, overall subject. This helps AI tools understand and generate clear, consistent, and complete content based on these documents. To optimize structured writing for AI, authors must adjust their content’s granularity. If topics are too long, authors can further split the content into smaller topics. If topics are too short — think, a single sentence — authors can assemble them together with intermediate maps to maintain a consistent level of information.

Adding structure to content allows new technologies like AI to better ingest information to then determine which topics are relevant to producing effective, accurate responses to user inputs.

2. Tag Content with Metadata

The next step in preparing your company’s content for AI tools is to add metadata. Metadata is simply data that describes other data, and it may comprise dates, numbers, or keywords. There are two main types of metadata to add to your documentation:

• Content management metadata includes tags describing topic creation or last modification dates, content authors, and publishing statuses. 
• Editorial metadata refers to the context of the content. For example, this includes tags about the product version, level of expertise, and task or user role (i.e. installation, maintenance, etc.).

Connecting structure and metadata, metadata tags must apply to all content within a structured topic. Adding metadata helps AI tools and chatbots give meaning and context to content to provide more accurate results with information relevant to the user’s needs. For example, there may be two similar documents focusing on the different parts of a product. However, one document has topics labelled with “installation” metadata while the other has topics labelled with “maintenance” metadata. Depending on the user’s query and needs, this information will help AI tools generate more relevant responses.

3. Add Semantic Markup

Once your content is structured and tagged, you should add semantic markup to the text.  Tag text with semantic labels to help computers identify and extract information like names, dates, quantities, events, products, components, technologies, measurements, and more. Adding semantics and schema markup is well known in the world of marketing and SEO, but this technique is valuable to product and support documentation as well. 

This is necessary because computers don’t have life experiences that provide meaning to information. Therefore, they need clear instructions to provide context and meaning. 

Adding semantics to content ensures an AI-enabled chatbot or virtual assistant would be able to guide a user through simple product set-up steps. As a result, your content should go from this:

To this, with semantic tags in the source text:

The end-user reading interface will hide the tags, so it’s easy for humans to read while simultaneously providing everything a computer needs to build a knowledge graph and extract relevant responses from the content.

4. Centralize Your Content

Centralizing content is the final step in optimizing your content so AI tools can access and use specific product information. As mentioned in the introduction, the quality of an AI solution depends on the content feeding it, so providing AI with seamless access to all relevant content is essential. 

However, as the number of content authors multiplies, so does the number of content authoring solutions. The result is content silos where information is scattered. By bringing content together into a unified hub of information, you create a single source of truth. This makes it far easier for content teams to manage, update, and deliver knowledge to the necessary channels, including new end user channels like an AI-powered chatbot. 

Rather than manually reformatting and adding content from across silos into a single authoring system, companies can simply invest in a Product Knowledge Platform. These platforms create a unified foundation between content sources and endpoints that aggregates content and makes distributing up-to-date content easy and efficient. Without unified content, delivering consistent, up-to-date information to AI tools becomes inefficient and error prone. Centralization is the final key to enhancing your content and launching new AI projects.

Conclusion

By combining structure, metadata, semantic enablement, and centralized content, companies optimize their content for AI tools to ingest and understand the information. Businesses that prioritize these four elements will launch AI initiatives more smoothly and see better returns than companies whose content isn’t prepared for AI.

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