As generative AI reshapes how people research, learn, and make decisions, a new discipline is emerging: Generative Engine Optimization (GEO). In Part 1, we have introduced the basics and the differentiation between GEO and traditional SEO. Today, we will walk you through the practical steps for an effective GEO.
Learn More:
Generative Engine Optimization (GEO) 101: Rank High in AI Search Results
How to Get Started with GEO?
These are the key steps for beginners to build your GEO strategy.
1. Benchmark current GEO performance
First you need to understand your starting point: insights into how your brand is discussed by all major Generative Engines, including ChatGPT, Claude, Gemini, Perplexity, Grok, and Deepseek.
2. Audit online footprint
We know that LLMs pull content from the web, which they use to inform their responses. So whatever is said about your brand online will significantly affect how Generative Engines talk about your company.
Start with your own website, this is the first place LLMs will look for information about your organization. But also focus on trusted, high authority third party sites like the mainstream news and specialist media within your industry, because these are the kinds of sources that LLMs will use.
Once you have a comprehensive audit of how your brand is currently portrayed online, you can perform a gap analysis against your desired messaging, and build a plan for how to reach that objective.
3. Map audience queries
An important difference between GEO and SEO is understanding how people search differently when using Generative Engines. Users interact conversationally with AI, and are likely to ask more complex questions in natural language, compared to simple keywords or short queries in a search engine. So it’s useful to get an idea of what kind of questions people will ask about your brand or industry, to help plan your GEO content. You can look at popular keyword searches associated with your brand, and brainstorm the most likely questions people will ask Generative Engines.
4. Align content to intent, not keywords
LLMs prioritize sources that provide comprehensive, well-structured, and contextually rich content. Focus your content strategy on:
- Educational depth: Long-form resources that answer questions end-to-end. Publish explainers, FAQs, and data-backed insights that answer “how” and “why.”
- Credibility signals: Citations, quotes, and data from reputable sources (analyst studies, thought leadership, or trusted media).
- Rich media: Generative Engines pull information from videos and images as well as text content, so make use of varied content formats.
- Consistency across channels: LLMs learn from media, social, and owned content together; ensure your narratives are aligned across them.
5. Measure, iterate, improve
Armed with the above information you can identify gaps in your GEO strategy where you might need to create more or better content. You can also discover which third party sources have the greatest impact on your GEO performance, and use that information to optimize PR outreach to focus on the most important media.
Like SEO, this is a process of constant iteration and improvement. GEO should not be seen as a one-off project that will generate positive results in perpetuity, but rather an ongoing activity that needs to be integrated with the broader marketing mix.
This article is originally published by Meltwater:
Meltwater provides social and media intelligence. By examining millions of posts each day from social media platforms, blogs, and news sites, Meltwater helps companies make better, more informed decisions based on insight from the outside. Learn more at meltwater.com.
Generative Engine Optimization FAQ
1. What success metrics should businesses track to evaluate the impact of Generative AI–driven search engine optimization?
Key GEO success metrics include:
- AI visibility share: How often a brand appears in LLM-generated answers.
- Sentiment and narrative accuracy: The tone and context of brand mentions.
- Engagement outcomes: Downstream traffic, conversions, or inquiries linked to AI-assisted discovery.
- Reputational indicators: Frequency of misinformation or outdated content.
2. What are the most common GEO mistakes?
Common mistakes include treating GEO like traditional SEO, relying too heavily on keywords instead of answering real user questions, and failing to monitor how LLMs currently describe the brand.
Other pitfalls include outdated or inconsistent messaging across channels, low-quality earned media coverage, and ignoring which sources AI platforms actually cite. Many teams also overlook recency—LLMs strongly favor fresh content—and fail to track narrative accuracy over time, leading to blind spots in how their brand appears inside AI-generated answers.
