Nearly three quarters of young adults in Canada use generative AI for work or study, a figure that helps explain a new gap between use and understanding. A KPMG Canada survey found the high uptake. A KPMG International study in June 2025 placed Canada fourth-lowest out of 47 countries for AI training and literacy. The result is a practical problem: people increasingly treat conversational AI as a substitute for primary documentation, and the tools can give plausible but incorrect answers. Institutions and documentation teams need clearer rules, validation habits, and design practices so AI accelerates learning rather than replacing it.

The practice of reading primary documentation is eroding, because students and novice users are treating generative AI as a conversational shortcut.

1. Diagnose how you use AI

Generative AI is often a scaffold, not a final authority. The pattern shows up in classrooms and workplaces alike: users prompt a model to explain a concept or to hand them a ready-to-run command, and then stop there. A student quoted by CBC, Elaine Xiao, said she turns to ChatGPT during study sessions because it "really talks to you in a way that's easy to understand," and she treats the output as a springboard rather than an endpoint. That distinction matters. If you open AI to clarify a hard paragraph, that's productive. If you rely on it to replace checking the version, edge cases, or a configuration parameter in the source document, you are introducing risk.

Start by cataloguing your habits. Ask yourself which of the following describes you: First, you use AI to explain concepts and then consult the primary documentation. Second, you use AI to fetch exact commands or snippets and apply them without verification. Third, you paste output from AI directly into production tests or assignments. The second and third patterns are the ones that accelerate mistakes.

2. Use AI for triage and summarization, not as the final source

Triage and summarization are where current models shine. Documentation teams and vendors are already exploiting that strength. Doctave has documented experiments where authoring tools based on Codex-heritage models and assistants such as GitHub Copilot speeded the production of comments and rewrites, while conversational interfaces increasingly answer natural-language questions about product docs and return exact commands or configuration lines.

That convenience is useful when it reduces the time you spend searching a long manual for the relevant section. Here is a worked example. Suppose you inherit a 200-page API spec. Ask an AI to list candidate sections that mention authentication. Use the AI output to jump directly to those sections in the original spec, then read the two or three authoritative paragraphs that define the token format, expiry, and scope. The AI helped you find the likely answers quickly; the primary document supplied the definitive parameters.

3. Validate every actionable answer against the primary document or a live test

Verification isn't optional. Document-processing research from Docupipe shows why: models can hallucinate, miscount, omit critical sections, and provide confidently wrong answers on verifiable tasks inside documents. Docupipe reported that 86 percent of a webinar audience had observed AI making things up or missing critical information when applied to document tasks. If an AI returns a configuration line, a compliance threshold, or a code snippet, open the original document and confirm the values.

When possible, run the command in a safe environment or run unit tests on the snippet before deploying.

Worked example: an AI gives you a curl command that claims to hit version 2 of an API. Check the spec to confirm the base URL and the versioned path, then run the request against a staging endpoint to confirm header names, expected status codes, and sample responses. If any piece differs, update your notes and notify the documentation owner.

4. Preserve reading skills with targeted practice

Deep reading is a muscle. Technical writers interviewed by Technical Writer HQ warn that AI amplifies sloppy information architecture and makes sloppy readers miss nuance. Allison Hoffman, a process documentation specialist, said the same workflows that are lax without AI will simply scale with AI. The remedy is disciplined practice that trains you to see assumptions and edge cases.

Mastheads

Do one full pass through a new specification instead of only skimming summaries. Map the table of contents to the sections you think matter. Extract definitions, preconditions, and explicit assumptions. Worked exercise: pick one specification and spend 30 minutes mapping each TOC entry to the single paragraph that confirms whether a change there would break an integration. That practice helps you judge whether an AI's short summary captured the nuance.

Dual design means predictable structure and canonical snippets. Documentation managers interviewed by Technical Writer HQ stressed the need for traceable content, versioning, and clear examples. John Francis, leader of technical documentation at Cisco, said current models need better context to give reliable answers and pointed to natural-language search that helps users refine queries. Keith Grigoletto, documentation manager at Curtiss-Wright, emphasised predictable organisation and traceable content as prerequisites for safe AI assistance.

Practical steps include: label blocks of code with metadata, keep canonical examples in a single source of truth, and publish changelogs that record what changed, why, and when. A documentation repository that supports explicit version tags and machine-readable examples lets an AI retrieve exact snippets instead of inventing plausible but incorrect alternatives.

Human review remains the safety net. Technical Writer HQ summarised a field consensus: AI speeds good workflows and scales bad ones. The right system treats AI output as a draft that requires human sign-off. Documentation teams should define metrics that show AI improves accuracy and speed without increasing regressions. Use spot checks, automated tests for code examples, and review gates for sensitive content.

Worked example: when an AI generates code snippets for a developer portal, run an automated test suite that executes each snippet in CI. If the snippet fails, flag it for reviewer action. Track two metrics over time: 1) the percentage of AI-created snippets that pass automated tests, and 2) the rate of post-deployment corrections traceable to AI-suggested content. Those numbers tell you whether the assistant is helping or harming.

Policy and education matter because Canada currently has high uptake and low literacy. The KPMG Canada survey found widespread use of generative AI among young adults, while KPMG's June 2025 international study placed Canada fourth-lowest out of 47 countries for AI training and literacy. The KPMG analysis found many Canadians lack confidence using AI and want clearer education, oversight, and trustworthy institutions to guide adoption.

Institutions should publish explicit policies on acceptable AI use, provide training on AI limits, and fund basic literacy modules for students and staff. Rob Clayton, KPMG's national education lead in Canada, told CBC that institutions need to be explicit about when and how students can use AI so it accelerates learning rather than circumvents it. Universities and research bodies are trusted actors according to the KPMG study, so their guidance carries weight.

Worked example: a university computer-science department publishes a one-page rule set that says: First, AI may be used for brainstorming and summarization. Second, any code or configuration derived from AI must be tested and annotated with the source. Third, instructors will specify when AI-assisted work is acceptable, and students must document their verification steps.

Simple checks catch many failures. Docupipe's analysis shows that models often miscount or omit sections. For developer-facing docs, embed runnable examples and unit tests. For numeric or compliance tasks, add checksum-style verification steps that a user can run to confirm totals. These small checks convert a black-box answer into a verifiable step.

Worked example: include a one-line test after each code block that users can run to validate the expected output. For tables of financial thresholds, include a reproduced calculation or checksum that users can recompute quickly. Docupipe's webinar respondents found these checks reveal where models go wrong.

The sources agree on the danger but focus on different fixes. Doctave documents a rapid product push toward chat interfaces and developer convenience. Docupipe highlights hallucination and the need for verification. Technical Writer HQ focuses on governance and process before tooling. KPMG places the problem at the national level by showing a literacy gap that makes institutional fixes more urgent. There's no universal prescription for mandatory training hours or regulatory deadlines across the sources, so organisations must prioritise the combination of policy, measurement, and hands-on practice.

First, track how often you accept an AI answer without opening the source document. Second, make validation mandatory for any actionable item the AI produces. Third, require human sign-off for sensitive content and automated tests for any code an AI suggests. Fourth, publish clear rules on acceptable AI use in your team or class and include a short literacy module on AI limits. Fifth, add runnable examples and checks to your documentation so users can confirm outputs quickly.

1. Nearly three quarters of young Canadian adults use generative AI for work or study, but Canada ranks near the bottom for AI literacy according to KPMG. 2. Use AI for triage and summarization, then verify with the primary document. 3. Make validation a mandatory habit: run tests, check changelogs. Require human sign-off. 4. Documentation teams must design for both humans and AIs with predictable structure and canonical snippets. 5. Institutions should publish clear policies and provide basic AI literacy training, as Rob Clayton and the KPMG studies recommend.

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Ask an instructor or manager for explicit guidance on AI use, and make verification routine: when an AI supplies a command, snippet, or interpretation, open the primary documentation and run or check the example before you act.

This article was created with AI assistance.