AI training for technical documentation teams

Build the workshop around recurring revisions

Syntalith proposes team training built around the path from product notes to documentation a user can read. During preparation, we establish where work stalls: repeated questions about a feature's scope, difficulty organizing the material, or feedback that gets lost between drafts. That determines the exercises and how the work is reviewed. The company contributes product knowledge and documentation expectations; Syntalith develops exercises using AI with those materials.

The scope can include outlining an article, drafting from an expert's replies, and updating existing documentation after a product release. Working on an author's own draft makes feedback specific: is a prerequisite visible, does a term match the product, and can the reader understand what the feature does? A later independent task shows which revisions the author can apply to different material.

Ask the expert more focused questions

A product note may be sufficient for someone who knows the feature while leaving an author without information the reader needs. Part of the workshop therefore concerns choosing an audience and planning the text before AI expands brief notes into several paragraphs. The author compares possible structures and decides which user question a passage needs to answer.

If a note merely mentions data export, a useful question asks who can perform it and in which versions. The answer belongs where the reader needs it in the draft. A task guide also requires questions about the actions to take in the product. This gives the expert a specific passage to review, instead of a broad request to explain the feature from scratch.

The same workshop can address shortening long explanations and using consistent terminology. Authors compare AI suggestions with company terms and neighboring documentation. The editor considers whether a change helps the reader, while the expert checks the feature description.

What specific feedback changes in a draft

Suppose a product note says that, in version 4, administrators can export change history as CSV. The AI draft encourages every reader to export it, omitting the role and version. A useful revision reads, “In version 4, administrators can export change history to a CSV file.” The condition appears immediately, helping users recognize whether the description applies to them.

The review addresses that editorial choice: the audience restriction needs to be visible before readers treat the passage as instructions for themselves. The author receives feedback on a particular part of the draft, revises it, and checks how it fits the article. A later independent task can use an updated note saying that version 5 gives regular users PDF export: the author needs to reconsider the sentence while retaining the new role, version, and format.

Use the tools already available

Oxygen Content Fusion's AI Positron documentation describes rewriting, readability improvements, review, and comparing suggestions before applying them. It requires the Author role and an installed, configured plugin. If the company provides these features, training can incorporate work in the existing editor.

For repetitive descriptions, a better template with space for the audience and product version may be sufficient. Training is relevant when authors need practice planning and editing varied material and working with experts. Choosing a local environment for drafting descriptions is a separate decision, covered in our article on local models for Polish technical descriptions.

Who to involve and where to start

Authors need time to write and revise their own text. A product expert and editor can join the discussion of selected passages where their input helps establish scope or improve the explanation. We consider writing experience and AI familiarity separately. An experienced writer may be learning to work with an AI draft, while a confident tool user can tackle longer notes or changes across several related passages.

The program is agreed with the team, including the exercises and any writing between sessions followed by a review. Fictional material can support the work; company documents must be approved for use in the chosen tool.

To begin, describe a text that repeatedly came back from an expert or editor and the revisions it needed. That provides a basis for deciding which part of the work training should cover and who should join the discussion. Service information is available on Syntalith's pricing page.