for public-sector communications and content teams
A half-day, hands-on workshop that teaches communications and content teams how AI answer engines choose their sources, and how to make official pages the source those engines cite. Every worked example comes from the participating organisation's own pages and questions.
When someone asks ChatGPT, Gemini or Google's AI Overviews about a public service or a policy, the engine writes the answer from sources it trusts. Often those sources are not the official pages. The engine quotes a news article, an aggregator or a forum that restated the information, and the restatement is what the reader sees.
For a public publisher this is an accuracy problem, not a marketing one. Restatements go stale, lose nuance or are simply wrong. This workshop teaches teams how AI engines decide what to cite, how to diagnose where their own pages stand, and how to write and structure public information so that the official source is the one quoted.
Built for the people who write and publish the pages, not for engineers. Nothing in the session requires access to a codebase.
The four structural shifts AI search introduces. Why a page can rank first and still never be read. What "citation as currency" means for a public publisher.
A shared vocabulary for the rest of the session.
Map any question into one of five states: not mentioned, mentioned but not recommended, cited with details wrong, a different source recommended, or recommended cleanly. Then choose the fix that matches the state, not a checklist.
Participants diagnose a page instead of guessing at it.
The three pillars: structure, entity authority and answer-first content. llms.txt, AI-crawler access rules, page rendering and firewall behaviour. FAQPage and GovernmentService structured data.
A technical checklist to hand to the web team or vendor.
Why AI engines corroborate before they recommend. The earned signals that matter for government: Wikipedia, Wikidata, news media and references from other public agencies. Sequencing earned signals before owned.
Earned-signal priorities, ranked for one content area.
Treating GEO as SEO with more keywords. Testing with a single prompt. Confusing AI Overview impressions with citations. Publishing a PDF where an extractable web answer is needed.
A do-not-do list, agreed in the room.
Answer first, evidence second, source third. The question patterns that win citations. Writing plainly without losing legal or policy precision.
Every participant rewrites a real paragraph, peer-reviewed live.
Question-shaped headings, an FAQ block, structured data and internal links, applied to a real page. Peer review against citation criteria, not against taste.
A revised page brief the team can take away and publish.
Build and lock a set of questions. Track citations across five AI engines each month and sanity-check the tools. Read Search Console AI Overview data without over-reading it.
A tracking sheet, populated before anyone leaves the room.
Ownership across communications, content, web and vendor. A cadence for question review, monthly tracking and escalation. What to do when the fix sits with a third party.
A named owner against every item on the fix list.
Explain how AI answer engines select and cite sources (ChatGPT, Google AI Overviews and AI Mode, Gemini, Perplexity, Copilot) and how this differs from search ranking.
Diagnose how AI currently treats a question or page using the five GEO States.
Choose the fix that fits the diagnosis: earned visibility, corroboration, owned content and structured data, or entity positioning.
Specify the technical requirements for structured data, AI-crawler access and rendering to request from a web team or vendor.
Prioritise earned signals such as Wikipedia, Wikidata, news media and cross-agency references for a content area.
Write and rewrite public information in an answer-first format that AI engines can extract, without losing policy precision.
Apply the Rewrite Framework to turn a live page into a publish-ready brief.
Run a monthly citation-tracking routine and assign clear owners for ongoing GEO work.
Up to 25 participants. The session works best when the people who write the page and the people who publish it are both present.
The core audience. They write and own the pages, and every hands-on module is pitched at them. No technical prerequisite.
Especially for Modules 3 and 7, which cover the structured-data, crawler-access and template decisions that content officers request rather than make.
The answer-first rewrite changes where the qualification sits in a sentence. Having the reviewer in the room saves rounds of revision later.
Module 8 sets up the tracking sheet live. If that person is absent, the tracking rarely survives the first month.
One organisation per cohort. Running each cohort for a single organisation means the worked examples are that organisation's own pages and questions, not generic case studies.
A half-day only works if the background reading happens before it and the habits are locked in after it.
45 minutes
3.5 hours
30 days
Total participant time: 45 minutes before, 3.5 hours live and one hour at day 30.
All materials are documents and workbooks the organisation owns outright. Nothing sits behind our tools, and there is no licence to renew.
The nine modules, the frameworks and the writing patterns, with public-sector examples.
The set of questions used in the session, as a spreadsheet the team can extend.
The monthly tracking template, populated during the session.
A one-page framework participants can apply to any page.
For participants who could not attend and for future intakes, with internal re-use rights.
The full deck as a PDF, for internal circulation.
Founder and Group CEO, Hashmeta
Architect of the 5 GEO States diagnostic and the citation scorecard methodology. More than 15 years in performance marketing across APAC, and he delivers public-sector engagements personally.
Founder of ZBiz Solutions
Architect of AI-powered marketing and digital growth strategies. Specializes in CRM automation, SEO, GEO, AEO, and data-driven scaling for online brands, and delivers public-sector engagements as co-trainer.
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