How to Do Generative Engine Optimization: A Step-by-Step Guide

What does each step produce?

Step 1: Set the scope

Decide what you are optimizing for before you measure anything. List the products or services that matter most to revenue, the customer segments you want to win, and the markets and languages you sell in. Then choose the engines your buyers use. For most businesses that means ChatGPT, Google's AI Overviews and AI Mode, Gemini, Copilot and Perplexity, weighted by what your customers report. A narrow scope measured well beats a broad scope measured badly.

Step 2: Build a set of buyer questions

Collect the questions buyers actually ask:

• Sales and support: questions prospects ask before buying, and objections they raise.
• Search data: longer queries in Search Console, and grounding queries in Bing Webmaster Tools' AI Performance report.
• Customer language: words customers use in reviews, emails and calls.

Group questions by stage (learning, shortlisting, comparing, checking) and by topic. Include category questions that do not name you ("best payroll software for restaurants") as well as brand questions ("Is [brand] good for restaurants?"). Unbranded questions show whether you are found; branded ones show whether you are described correctly. How to organise them is covered in how to bucket AI tracking prompts by commercial intent and buying stage.

Step 3: Measure a baseline

AI answers vary between runs, so one check per question is not enough. Run each question several times in each engine, ideally ten or more for important questions, using consistent settings and locations. For each run, record:

• whether you are named, and in what terms;
• which competitors are named;
• which sources are cited;
• any factual errors about you.

Your main measure is the appearance rate: the share of runs in which you are named. Add an accuracy rate for how often the description is correct. Keep the raw answers, since you will need them for diagnosis.

Step 4: Diagnose the gaps

Sort what you find into four kinds of gap:

1. Access gaps. AI search bots are blocked by robots.txt, a firewall or a CDN, or content only appears after scripts run. Check server logs and page source.
2. Clarity gaps. Assistants describe you vaguely or wrongly. Compare your descriptions, prices, locations and features across your site and your most-cited profiles.
3. Content gaps. Questions where you have no page that answers directly, or where your page is general and a competitor's is specific.
4. Source gaps. Cited domains where you are absent or misrepresented, such as review platforms, directories, comparison articles and news sites.

Rank the gaps by how many priority questions each affects and how hard it is to fix.

Step 5: Fix access and facts first

These fixes are cheap and fast, and they often move accuracy within weeks:

• Allow the AI search bots you want in robots.txt, firewall and CDN settings.
• Make sure key text is in the page HTML.
• Write one plain description of what you are and use it everywhere.
• Correct prices, hours, locations and features on your site, Business Profile, directories and review platforms, and remove or redirect outdated pages that contradict them.

Step 6: Improve the pages assistants need

For each content gap, create or rewrite a page that answers the question directly:

• Open each section with the answer, then give the detail.
• Include specifics: prices, specifications, integrations, locations, timelines and limits.
• Add something others lack, such as your own data, examples or tests.
• Cover the page types buyers use when choosing: pricing, comparisons, alternatives, use cases, integrations and locations.

Avoid mass-producing near-identical pages for question variations. Google's spam policies treat that as scaled content abuse. What these pages look like for software companies is covered in GEO best practices for SaaS companies.

Step 7: Earn presence in the sources assistants cite

Third-party sources shape many answers. In Omniscient Digital's study of branded-question citations, more than half (about 57%) pointed to reviews, lists, forums, social posts or case studies rather than company pages. Work on:

• Reviews on the platforms cited in your category, requested from all customers and within each platform's rules.
• Directory and marketplace profiles, complete and consistent.
• Comparison and "best of" articles that assistants cite often. Peec found that B2B software brands ranked first in frequently cited lists were mentioned about 16.5 percentage points more often than brands lower down.
• Press and expert commentary built on real news or data.
• Communities where your customers talk, taking part openly as the company, never through fake accounts.

Step 8: Remeasure and report

Run the same question set monthly with the same method. Report:

• appearance rate by question group, against competitors;
• accuracy rate of descriptions;
• pages and third-party sources cited;
• AI referral visits from Google Analytics 4's AI Assistant channel, and AI impressions from Search Console's generative AI report;
• branded search trends and leads that mention AI.

Compare questions you worked on with similar questions you did not, so changes in the engines themselves do not mislead you.

How to build a GEO strategy for AI-assisted search in 2026

A strategy decides priorities, owners and budget around the steps above:

• Priorities. Start where revenue and gaps overlap: high-value questions where you are absent or misdescribed. Accuracy problems come first, since a wrong answer does more harm than a missing one.
• Owners. SEO or content usually leads, with named contributors in PR, product marketing, customer success and web development. Reviews, press and product facts do not sit with the SEO team.
• Budget. Split effort between on-site work (pages, facts, access) and off-site work (reviews, listings, coverage). Many teams underfund the second.
• Timeline. Expect fact and access fixes to show within weeks, page improvements within one to three months, and third-party presence over two to three quarters.
• Risks. Engines change often. Citation volumes in ChatGPT, for example, dropped sharply at platform level in spring 2026 before recovering. Report trends over several months, not single readings.

What GEO means in general is covered in what GEO means for AI visibility and citations.

What might a worked example look like?

Consider an illustrative accounting firm serving small hospitality businesses in two cities. It builds 40 questions, from "accountant for restaurants in [city]" to "how much does a bookkeeper cost for a café?". It runs each ten times across five engines. It appears in 6% of runs, and two assistants describe it as a general accountancy practice. Diagnosis finds its site never states the hospitality focus clearly, its directory listings show an old address, and assistants cite two local business directories and a regional "best accountants" article where it is absent. Over three months it rewrites its home and service pages around hospitality clients, publishes prices, fixes its listings, asks every client for an honest review in their own words and contacts the article's author with specific information. Its appearance rate rises to 22%, and descriptions become accurate.

What do people ask about doing GEO?

How long does GEO take to show results?

Accuracy fixes can show within weeks. Consistent gains in how often you are named usually take three to six months.

Can a small business do GEO without an agency?

Yes. The steps are the same; a smaller question set and a spreadsheet are enough to start.

Which step do most teams skip?

Measurement with repeated runs, and off-site work. Without the first, results are guesswork; without the second, gains are limited.

How often should the question set change?

Keep a stable core for trend reporting, and add new questions each quarter as products and buyer interests change.