How to Do Generative Engine Optimization, Step by Step

To do generative engine optimization, choose the prompts your buyers actually ask AI assistants, map each prompt to one page that answers it in a short, quotable passage, back that page with first-party evidence and third-party mentions, make sure AI crawlers can read it, then retest those prompts on a fixed schedule and refresh whatever stops being cited. The rest is execution detail, and execution is where most programs stall. This guide shows how to do generative engine optimization as a sequence a senior SEO or demand gen team can run: prompt research, prompt-to-page mapping, passage design, technical readiness, earned mentions, testing and measurement. It reflects how ChatGPT, Perplexity, Gemini, Claude and Google AI Overviews retrieve and cite sources as of October 2026. It also says plainly where a self-serve tool fits better than an agency.

What generative engine optimization is and how it works

Generative engine optimization (GEO) is the practice of shaping content and off-site presence so AI systems such as ChatGPT, Perplexity, Gemini, Claude and Google AI Overviews retrieve, summarize and cite your brand in their answers. The term covers optimizing for AI-generated responses rather than a list of ten blue links. It has also moved from research concept to budget line, with more brands turning to generative engine optimization to get noticed by AI bots.

The goal differs from classic rankings. You want the model to describe your brand accurately, name it in shortlists, and link to your pages as a source.

How does generative engine optimization work?

Most AI answers that cite sources come from retrieval-augmented generation (RAG). The model searches an index, pulls passages, and writes an answer grounded in them. The process runs in four stages:

1. Query fan-out. The engine splits one prompt into several sub-queries. "Best cloud security platform for a 2,000-person fintech" may become searches for "cloud security platform comparison", "CSPM for financial services" and "SOC 2 cloud security tools".
2. Retrieval. Each sub-query pulls candidate pages from a search index (Google's, Bing's or the engine's own).
3. Passage selection. The system scores chunks of those pages, not whole pages, for relevance and clarity. A tight 60-word answer beats a 900-word intro that buries it.
4. Synthesis and citation. The model writes the answer and attaches links to the passages it leaned on.

Answers without retrieval come from the model's training data, which is months old. That is why an assistant can describe a brand with an outdated product name or pricing model. The answer comes from stale model memory rather than a live page.

How retrieval differs across platforms

GEO vs SEO vs AEO

GEO adds a second scoring layer on top of SEO and leaves SEO in place. AEO (answer engine optimization) is a near-synonym that grew out of featured-snippet work, and most teams now use GEO and AEO interchangeably.

How to do generative engine optimization: a step-by-step process

A working GEO program runs eight steps in order, with a decision point after measurement that loops back into content. Each step below names the output it should produce.

1. Set the category and competitor set. List the five to ten competitors that buyers actually compare you with, plus the review sites and communities that shape the category (G2, specific subreddits, analyst pages). Output: a one-page category map.
2. Collect real prompts. Pull language from sources buyers already use:
   • Sales call recordings: the questions prospects ask in discovery ("does it work with AWS Organizations?").
   • Support logs and chat transcripts: problem-phrased questions.
   • People Also Ask boxes and Google autocomplete for your category terms.
   • Follow-up suggestions that ChatGPT, Perplexity and Gemini offer after a category prompt.
   • Reddit and forum threads, which show how buyers phrase comparisons in their own words.
   Output: 150 to 300 raw prompts.
3. Cluster prompts by intent. Group them into informational ("what is CSPM"), commercial investigation ("best CSPM for multi-cloud", "X vs Y"), and transactional or brand prompts ("X pricing", "is X SOC 2 compliant"). Keep 30 to 60 priority prompts for tracking. Output: a prompt set with intent labels.
4. Baseline visibility. Run every priority prompt across the engines that matter to your buyers and log who is mentioned and which URLs are cited. Output: a baseline sheet of citation share per prompt.
5. Map prompts to pages. Assign each cluster exactly one target URL. If two pages compete for the same prompt, merge them or split the intent. Output: a prompt-to-page map (example below).
6. Build or rewrite the pages. Apply the passage design in the next section: answer blocks, tables, evidence, author credentials, dated facts.
7. Earn off-site mentions. Look at the domains that get cited for your target prompts in the baseline. If Reddit threads and comparison listicles dominate, your own pages alone will not move the number.
8. Retest, then decide. Rerun the prompt set weekly or biweekly. If a page ranks organically but is not cited, fix passage structure. If it does not rank for the fan-out sub-queries, fix SEO first. If competitors win through third-party sources, shift effort to mentions.

Example prompt-to-page map

Generative engine optimization strategies and best practices for content

The core generative engine optimization strategies come down to writing passages a model can lift whole, on pages that carry evidence and clear entities. For the full page-level method, see our guide to structuring pages a model can quote.

Design passages in five parts

1. Answer block: one or two sentences that answer the heading on their own, with no "this" or "as mentioned above".
2. Supporting detail: the conditions, thresholds or steps that make the answer specific.
3. Evidence: a figure, test result or customer detail from your own data.
4. Source: who measured it and when, named in the same sentence.
5. Summary: a closing line that restates the takeaway for skimmers.

Generative engine optimization examples: before and after

Before: "Our platform helps security teams work smarter with powerful, AI-driven insights that reduce risk across your cloud."

After (illustrative figures): "Cloud security posture management (CSPM) tools scan cloud accounts for misconfigurations such as public S3 buckets and over-permissioned IAM roles. [Brand] scans AWS, Azure and GCP accounts every 15 minutes and flags, for example, any IAM role unused for 90 days. In [Brand]'s 2026 analysis of 400 customer environments, the most common finding was unrestricted inbound SSH. Teams typically start with these three checks before enabling full policy packs."

The rewrite adds a definition, named entities (AWS, Azure, GCP, IAM, S3), a threshold, a dated first-party data point with its source, and a summary. Each sentence still makes sense when quoted alone.

Model outline for a GEO-optimized page

1. Title and H1 phrased as the prompt cluster ("Best CSPM Tools for Multi-Cloud Teams").
2. Direct answer in the first two sentences.
3. Definitions of category terms.
4. Comparison table with explicit criteria.
5. Numbered steps or evaluation process.
6. First-party evidence with methodology notes.
7. Sources and last-updated date.
8. Author name, role and credentials, marked up with Person schema.

Formats that get cited most

• Comparison and "best X" pages with clear criteria.
• Original research and statistics pages with a methodology section.
• Glossary entries that open with a one-sentence definition.
• Step-by-step tutorials with numbered steps.
• Expert commentary signed by a named practitioner.
• Question-led sections answering adjacent micro-intents.

Use first-party data

Models cite what no one else has. Proprietary surveys, anonymized usage statistics, benchmarks, and case studies with named outcomes give an engine a reason to cite you rather than a competitor's summary. Publish a methodology note with each, covering sample size, date range and how the data was collected. That note is what lets an engine quote the figure with confidence.

Strengthen entities

Entities are the named things a model connects: your brand, product names, founders and experts, category terms, and integrations. Use the same product names everywhere, define your category in one consistent sentence across your site, LinkedIn, G2 and Crunchbase, and link experts to their profiles with sameAs properties. Inconsistent naming produces blended or wrong descriptions.

Adjust by search intent

• Informational: definitions and tutorials, which win on clarity and freshness.
• Commercial investigation: comparison pages on your site, plus inclusion in third-party listicles and Reddit threads.
• B2B: trust pages (compliance, integrations, pricing model) answered directly, since buyers ask assistants about them before a demo.
• Ecommerce: Product schema with price, availability and review data, and the same specs across retailers and your own product pages.
• Local: accurate Google Business Profile, LocalBusiness schema and consistent name, address and phone across directories.
• YMYL (health, finance, legal): named, credentialed authors, cited primary sources and visible review dates. Engines are most conservative here.

Technical readiness for AI crawlers

If AI crawlers cannot fetch, render and understand your pages, no content work will get cited. Work through these checks, then use our GEO checklist of fixes to make first for the full list.

• Robots rules: decide crawler by crawler. GPTBot covers OpenAI training, while OAI-SearchBot and ChatGPT-User fetch pages for ChatGPT search and browsing. PerplexityBot indexes for Perplexity, ClaudeBot serves Anthropic, and Google-Extended controls Gemini training use without affecting Google Search or AI Overviews. Blocking a search crawler removes you from that engine's citations.
• JavaScript rendering: many AI crawlers do not execute JavaScript. Serve key content with server-side rendering or static generation, and check by fetching the raw HTML with curl.
• Canonicalization: use one canonical URL per answer, so citations and signals do not split across parameter or locale variants.
• Structured data: add Organization, Person (authors), Article, FAQPage, Product, Review and LocalBusiness in JSON-LD, and validate it with Google's Rich Results Test and the Schema.org validator.
• Internal linking: link glossary entries, tutorials and comparison pages into topic clusters so retrieval finds the strongest page for each sub-query.
• Freshness signals: show last-updated dates and set dateModified in schema, and make both match real edits.

How to measure GEO results and manage the risks

Measure GEO by retesting a fixed set of prompts on a schedule and tying citation movement to traffic and pipeline. Our guide to AI visibility metrics, prompts and baselines covers setup in depth. The essentials are below.

Prompt testing methodology

1. Freeze the prompt set and wording, because changing phrasing breaks comparisons.
2. Run prompts logged out or in fresh sessions to limit personalization and memory.
3. Control location (a VPN or the engine's region setting) and device, since AI Overviews vary by country and mobile vs desktop.
4. Run each prompt three times per engine. Answers vary between runs, so log mention rate as a share of runs.
5. Log date, engine, prompt, brands mentioned, order, cited URLs, and whether the brand description was accurate.

GEO measurement spec

Maintenance cadence

Refresh commercial comparison pages quarterly and statistics pages whenever the underlying data changes. A page has lost citation potential when its page-level citation rate drops across two consecutive tests, when a competitor's newer page replaces it in citations, or when it cites figures older than about a year.

Risks to monitor

• Hallucinations: invented features or pricing. Publish a clear, crawlable fact page and correct the third-party sources the model is citing.
• Stale model memory: old product names persist in training data. Consistent current descriptions across high-authority sites fix this over time.
• Citation volatility: answers change between runs and model updates, so judge trends over four to eight weeks, not single tests.
• Governance: AI-assisted content needs human review for accuracy and compliance before it ships, especially in regulated categories.

How Tellr fits into a GEO program

Tellr runs the steps above as one managed program for enterprise teams, so the tracking, the pages and the off-site presence are decided together instead of across separate vendors.

• Weekly citation-share tracking of Google and AI Overviews feeds the content plan for steps 4 and 8.
• Comparison pages and answer-shaped articles, built from your knowledge base and real user reviews, publish straight to WordPress.
• Reddit replies cover step 7, checked against subreddit guidelines and held behind an approval gate.
• A weekly digest and a monthly program review report articles shipped, replies still live and citation movement.
• "Tellr for Claude", an MCP server, lets your team query the program in plain language.

Tellr is priced as a premium managed program for teams spending $10k+ a month, so smaller teams are better served by a self-serve tracker.

Generative engine optimization tools, services and agencies

Generative engine optimization tools measure where you appear in AI answers, while a generative engine optimization service or agency also produces the content and mentions that change it. Most generative engine optimization companies fall into one of those two camps, and the right choice depends on whether your team has the capacity to act on the data. G2 ratings below are as of October 2026.

Tellr

Tellr is a premium earned-visibility agency that covers four jobs: Reddit marketing, content built for AI search, AI and search visibility tracking, and paid-media creative. For each tracked query, it labels every domain that Google and its AI Overview cite as yours, a competitor's, Reddit, social, review sites or references, and shows week-over-week movement. It writes and publishes the comparison pages and reviews that change those citations, and it refuses growth-hack tactics such as upvote buying, aged accounts or fake reviews. Collection runs through its own search-data pipeline, and it does not need GA4 or Search Console access to run.

Profound

Profound tracks brand mentions, citations, share of voice, sentiment, position and AI referral traffic, collecting answers through API, browser sessions and panel data, with a core index that updates weekly. It integrates with GA4, Search Console, WordPress, Slack, BI tools, an API and an MCP server, and offers a 7-day trial with 50 prompts per day. Reviewers praise its citation analysis and competitor benchmarking. The common complaints are high pricing and a steep learning curve, with data that can be hard to turn into action without internal processes.

Semrush AI Visibility Toolkit

Semrush adds AI visibility to an SEO suite many teams already own, tracking mentions, citations, share of voice and prompt position, with prompt rankings refreshed daily and brand data weekly. It integrates with GA4, Search Console, WordPress, an API and an MCP server. It suits teams that want AI tracking next to keyword and audit data. Reviewers flag limited regional and language coverage, add-on costs that scale quickly, and no closed loop from insight to published content.

Scrunch AI

Scrunch AI tracks mentions, citations, sentiment, position and AI bot traffic across seven engines, with a 7-day trial on its Starter plan. Reviewers like its quick onboarding, custom prompt tracking and GA4 integration. Its weaker points are export and reporting options, limited historical trend data, and insights that users say are not always easy to act on.

Whichever route you take, the method stays the same: a frozen prompt set, one page per prompt cluster, passages built to be quoted, evidence only you can publish, and mentions on the sites engines already trust. Teams that know how to do generative engine optimization this way can hand the tracking to a tool or the whole program to an agency without changing the playbook.

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