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Doc-as-Data

Considering Documentation as a Data Source for AI​

Documentation as a data source for AI

What is the purpose of software documentation?
Historically, it was designed for humans — developers, users, customers — to help them understand and use a software product.

But in the era of generative artificial intelligence, documentation is also becoming a data source.

It is no longer just text to be read, but a structured corpus — searchable, reusable, and exploitable by AI systems.
This is the concept of Doc-as-Data.

Looking Back at Traditional Documentation​

A Reference Resource and a Contractual Document​

Documentation is the official source of truth about the product.
It describes the software as delivered, for a specific version, and ensures consistency between what was designed and what is actually available.

  • It is first and foremost a contractual document: it certifies that the software is provided with a complete description of its features.
    This avoids explaining individually to every client how to use the product and protects the publisher in case of misuse (“RTFM”: Read The F***ing Manual).
    It may include a version page listing dates, responsible parties, and even translations into multiple languages.

  • It represents a snapshot of the product at a given time.
    In some cases, it becomes a “retro-spec”: when the original specifications are missing or unclear, the documentation itself becomes the source of truth about the actual state of the software.

In this approach, the format or usability doesn't matter — the information must be there, all of it, even if it's lengthy (and even if few people read it).

A User Assistance Tool​

Technical writers move beyond this rigid framework and become content architects. They design, organize, and optimize text and visuals to guide users through the product.

Documentation thus goes beyond its contractual purpose — it becomes a service.

It stands at the crossroads of training and customer support:

  • Well-structured documentation acts as a detailed training resource.
  • Documentation that integrates user feedback helps reduce support tickets.

It combines text, screenshots, and diagrams to help users quickly understand how the product works and how to use it efficiently.

The Challenge of Finding Information​

Before the Digital Era: Table of Contents and Index​

Legacy paper documentation offered two main ways to find information:

  • The table of contents, for structured navigation (chapters, sections, subsections);
  • The index, to locate keywords and check all related pages.

Online help systems later adopted both approaches, adding a full-text search feature.

Online help with table of contents, index, and search

The PDF format, allowing global search, marked a turning point: it made access to information faster without changing the nature of the content.

These tools addressed the need to find information, but not necessarily to understand or reuse it.

The Web Era: Navigation and SEO​

With the rise of web-based documentation, indexes often disappeared — too time-consuming to maintain.
Internal search engines aren't always exhaustive, and users are often referred to the browser's own search (Ctrl+F).

Web documentation site with integrated search

💡 RoboHelp
More than twenty years ago, RoboHelp already allowed the creation of web help systems with full-text search and manual indexes.
I liked it for that reason: it provided a consistent and rich user experience.

In modern documentation sites, both the main table of contents and page-level outlines are essential.
They offer an instant overview of the structure and help users locate what they need.

But there's a twofold risk:

  • A large part of the information may be ignored by users in a hurry;
  • Another part may escape search engines, if SEO is absent or poorly structured.

This limitation signals a paradigm shift: documentation must now be “readable” not only by humans but also by machines.

Documentation in the Age of AI​

Documentation as a Communication Channel​

Documentation is no longer just a technical deliverable — it contributes to the product's identity and reputation.
It should reflect the company's visual identity and tone of voice, just like the corporate website.

  • It is a core part of the customer success strategy: clear, enjoyable documentation builds user loyalty and reduces churn.
  • As a web product, it must be accessible, inclusive, and consistent with the brand message.
  • It also acts as a product communication tool, providing context and enhancing the user experience.

🧩 Personal Example
This very website, built with my favorite documentation framework — Docusaurus — serves as a lab for testing and implementing everything described here.

Visibility and GEO​

GEO (Generative Engine Optimization) aims to make documentation visible and trustworthy to generative AI platforms.

The goal is for AI-generated answers to rely on your official, validated, and high-quality content.
It's the natural evolution of SEO in a world where generative AI replaces traditional search engines.

Making Documentation “AI-Ready”​

To make documentation AI-ready means making it usable both by humans and by models.
This involves two complementary approaches: technical and human.

Technical​

  • Structure content with clear metadata and machine-readable formats (Markdown, JSON, structured schemas).
  • Organize documentation for RAG (Retrieval-Augmented Generation) systems and internal LLMs.
  • Design navigation and logical segmentation as units of information that can be processed by AI systems.

Human​

  • Adapt tone, vocabulary, and visuals to each audience (role, profile, or culture).
  • Localize documentation with regional and linguistic nuances (e.g., US vs UK English).
  • Use AI for human-like translations, style guide enforcement, and tone adjustments.
  • Offer accessible and mobile-friendly versions (inclusivity, voice reading, contrast, etc.).

Doc-as-Data – The New Paradigm​

Documentation is no longer a simple deliverable — it is a data corpus.
It describes the company, its products, its users, its technologies, and its ecosystem — and above all, it's exploitable.

This corpus includes:

  • Documentation websites,
  • Git repositories,
  • Internal wikis (Confluence, Notion, etc.),
  • And any space where the company produces reference content.

AI-Oriented Use Cases​

This data pool can be leveraged by several systems:

  • Internal chatbots, able to provide precise answers about products;
  • Enterprise LLMs, trained on internal documentation;
  • RAG systems, combining retrieval and generation;
  • Generative engines, indexing public documentation (GEO).

The Challenges of the Doc-as-Data Model​

Treating documentation as data means:

  • Ensuring the quality, consistency, and traceability of content;
  • Establishing governance: who validates, who updates, and how;
  • Designing documentation for interoperability, through APIs and open formats.

The technical writer thus becomes a data curator — crafting content that is readable and reusable, not only by humans but also by AI systems.

Toward Symbiotic Documentation​

Documentation has become an active part of the software lifecycle — both a product and data.
It addresses not only human users but also AI models that analyze, understand, and redistribute it.

The technical writer's role evolves: they now write for two audiences — human readers and artificial intelligences.
Doc-as-Data marks a new era: one of symbiotic documentation, at the crossroads of content design, data science, and product strategy.

© Author: Florence Venisse, STW – First version dated October 29, 2025