At Kapa.ai, we've partnered with several industry-leading semiconductor companies, including Espressif Systems, Nordic Semiconductor, and Silicon Labs, to enhance customer service using our AI assistant technology. What makes semiconductor companies stand out is their huge library of detailed technical information:

• User manuals

• Data sheets

• Hardware specifications

• Developer guides

• Application notes

• Errata sheets

• Material certificates

Example of Kapa-powered Nordic Semiconductor “Ask AI” on their documentation site

This article shares practical tips we've gathered from these collaborations. We'll cover several key areas to improve your AI assistant deployment, including how to:

• Handle PDFs effectively

• Preserve important backlinks for citation

• Structure content for better AI understanding

• Deal with tables and product selectors

• Manage different product versions

Handle PDFs effectively

PDFs (and other similar document formats, like Word documents) contain rich technical information but present challenges for knowledge extraction because the format is comparatively unstructured and difficult to parse.

Not all PDFs are equally unstructured; there are degrees of unstructuredness. Documents that incorporate clear headings, text content, and lists are significantly easier for AI extraction than those composed solely of photographic scans or image-based content. Even though PDFs are inherently layout-oriented compared to the semantic structure of HTML, the more you can impose structure on a PDF, the better the AI can extract and understand its content.

Structured information is always preferable to unstructured data. However, this is an active area of research, and continuous improvements are being made in AI's ability to interpret complex layouts.

Here are some pointers on how to make a PDF "well-structured" for optimal AI understanding:

• Ensure your PDFs have proper headings, sections, and a logical flow. The document should have clearly defined titles and subtitles that match what's visually presented.

• Break large documents into meaningful sections based on topic (a.k.a. topic-based authoring). Ideally, each section should make sense as a standalone piece of information.

• Include text descriptions alongside images and figures. Modern LLM systems based on retrieval augmented generation (RAG) still struggle to interpret visual content consistently. Ensure all diagrams, charts, and technical illustrations have accompanying text descriptions to make this information accessible to AI assistants.

Preserve important backlinks for citation

One of the most valuable aspects of Kapa is the ability to cite and backlink to the information sources. When ingesting content that isn’t from a web scrape (such as PDF documents uploaded directly to Kapa or through our cloud bucket integration), ensure that you maintain original source URLs or file identifiers for each article. You do this when setting up the data source on the Kapa platform.

Source citations build user trust because the AI answer is verifiable.

Structure content for better AI understanding

This is not specific to semiconductor content, but it’s worth reiterating:

• Use heading hierarchies to help AI models understand information relationships.

• Use short, descriptive headings that clearly indicate section content to improve retrieval precision.

• Define key terms in your documentation, and maintain consistency in terminology throughout your knowledge base.

Well-structured content helps the AI assistant understand context relationships that might be intuitive to human experts but need to be made explicit for machine learning systems.

A product-oriented hierarchy helps the LLM understand the context of information. (Silicon Labs)

For more tips, read our blog post about Optimizing Technical Docs for LLMs.

Deal with tables and product selectors

Tabular data represents some of the most valuable technical information in semiconductor documentation. They’re usually easy enough for a human to understand, but can pose a big challenge to AI comprehension. Here are some things we recommend you try to make data robot-accessible:

• Ensure column and row headers are clear and descriptive.

• Simplify or break down large, complex tables when possible. In some cases, using an alternate format such as sections or line item lists can improve retrieval accuracy compared to pivot tables.

• Avoid placing critical information exclusively in table format.

• Consider adding a textual explanation or supplementary narrative content that explains table keys and common comparison points.

Manage different product versions

One common characteristic for semiconductor companies is that they typically maintain multiple product generations and families simultaneously, and users often need to take into account software and hardware compatibility.

We highly recommend separating or partitioning your knowledge base for major product lines or generations. This helps Kapa refer to the correct source and not that of a sibling or parent product. In some cases you might even want to consider separate Kapa projects per product generation or family, to avoid accidental “cross-pollination” between them.

In cases where you have documentation that describes cross-product functionality, but some sections only apply to specific products or versions, mention that explicitly in the content, such as: “Available in versions X.Y and later”. Develop consistent patterns for handling version-dependent information.

Conclusions

There are lots of things you can do to help Kapa better understand your content. This article is more about optimizing than getting started. Don’t think of these recommendations as prerequisites - Kapa performs remarkably well out of the box, especially for semiconductor use cases where the information substrate is so large and well-organized.

Implementing these AI optimizations is best done iteratively: start small, measure the impact, and reiterate. Run a quick pilot project focused on a single product line. Use the Kapa platform analytics to determine which documentation areas need the most improvement, and to monitor user interactions and see the results. The most successful companies using Kapa have created a continuous cycle where AI assistant analytics directly inform documentation strategy, creating continuously improving knowledge resources that benefit both human and AI readers.

If you're interested in seeing how Kapa.ai can transform your knowledge base into an intelligent assistant, request a demo here.