Generative Engine Optimization Strategies: How to Optimize Amazon Content for AI-Powered Shopping

Imagine one customer shopped on behalf of 70 million people – how important would that customer be to your brand? 70 million people is the number of ChatGPT users in the US according to Nerdynav as of June 2025. And, with enhanced shopping features being rolled out by OpenAI earlier this year, trends are rapidly shifting from search-based to AI-powered shopping. With Amazon continuing its dominance at the top of online shopping, it’s only natural that they be at the forefront of this trend, primarily with their AI-powered assistant Rufus. In order to keep pace with this trend, brands must cater their content to ensure it is optimized for human and AI customers alike.

This article walks through the basics of Generative Engine Optimization (GEO) search optimization, providing tips and strategies that you can implement with relatively little effort to ensure your products are set up for AI visibility. Since there are two layers to the equation – one layer being Rufus inside Amazon’s ecosystem and the other being LLMs outside Amazon, like ChatGPT – we’ve structured two sets of tips to help with both.

Generative Engine Optimization Definition & Strategies

Similar to Search Engine Optimization (SEO), you’re optimizing content in order to surface higher within a system that is designed to recommend results. While SEO is centered around the rankings of various keywords, GEO is influencing the way in which AI assistants interpret information and recommend results based on the available data. While keyword research and usage were the lynchpin for SEO success, phrasing and data structuring become pinnacle for success with GEO. Thankfully this means the days of tactics like keyword stuffing are long gone and copywriters are incentivized to write more naturally – because the AI LLM system interpreting the content is designed and trained on natural language.

One important point to note is the complexity of data the AI models have access to. In the case of Amazon’s Rufus, we know that content throughout the Amazon site is the foundation for information. However, Amazon states Rufus can gather information from off-site sources, which leads to the need for better management of content across all potential sources. Your AI optimization efforts should be prioritized inside the Amazon system, but the people and teams running Amazon businesses have additional interest in high-quality, consistent content across all online sources, because it will influence the customer experience on Amazon directly.

3 Tips for Generative AI Search Optimization on Amazon Pages

1. Shift from heavily keyword-centric copy and speak (more) naturally

• This includes stating problems the product solves from the perspective of the customer versus just stating features.
• Copy should be structured in a way that balances conciseness while following standard grammar and punctuation. This means avoid using special characters and odd grammatical structures. While they might be helpful for actual customer legibility, it may confuse AI.
  • It’s important to keep in mind how AI LLMs are trained – typically with massive amounts of long-form text and data. If you structure your copy in a way that likely reflects the training data, the AI LLM (Rufus in this case) will have an easier time interpreting.

2. Keep your product attributes/metadata accurate and up to date

You can see in the example below how the Size attributes used are causing a less-than-ideal response from Rufus with the inconsistent nomenclature. While this is a fairly benign example, risk-takers who have pushed the envelope with their variation tactics or injected copy in fields they shouldn’t have, may find they’re causing Rufus to extract and relay bad information.

3. Include simple text in imagery when applicable

Rufus can extract information from product page images, so overlaying concise text on images with key selling points should be a best practice versus an aesthetic-first approach.

3 Tips for Generative AI Search Optimization Beyond Amazon

1. Spend time building your brand reputation on Reddit

A recent study showed that Reddit was the leading source cited by the most popular LLMs at just over 40%. Generate discussion about your brand and products to ensure AI models are viewing your brand positively and retrieving accurate information.

2. Consistency across potential sources is key

The image below includes an example of what can happen when information about a product is not accurate across websites. It can be a daunting task to ensure dozens to hundreds of websites have accurate data about your products, but it’s also critical to ensuring customers trust the info coming from the LLM they’re using to research your products. If they observe inconsistencies, the odds of them digging to find the right answer are likely low and you may lose a potential new customer altogether.

Added tip: You should regularly check for this by conducting product searches as your customers would. Use ChatGPT’s deep research function to help identify the potential inconsistencies across sites at scale.

3. Measure brand site traffic from AI and optimize your site

• Brands can see web traffic from the most popular LLMs via Google Analytics
• Similar to your copy approach with Amazon pages, ensure you’re structuring data on your site for machine-readability.

Where is Rufus Heading?

If you’re feeling experimental with your content, we’ve also provided a few hypotheses on the direction Rufus could potentially be heading towards:

1. Stronger video indexing for data

In our research, Rufus tends not to rely on data within videos that were on the PDP, but given it’s fairly solid ability to derive information from text in images, plan on catering your videos for AI. This includes:

• Preference to text overlay versus fancy graphics and visuals
• Longer videos explaining more features and benefits of the product
• Consider including subtitles so that AI has a transcript to draw information from

2. Granular tracking for customer satisfaction

Eventually Rufus could assist customers in their entire purchase experience with a brand. Here’s one walk through of what this may look like:

1. Customer discovers product with Rufus
2. Customer asks specific questions about it (eg: “How long will this keep my drink cold?”)
3. Customer purchases product based on stated features and information from Rufus
4. Customer uses the product and Rufus follows up a while later (eg: “Did the product keep your drinks cold for as long as expected?”)
5. The outcome reinforces Rufus on its understanding and perspective of that product/brand

3. Formal process to train Rufus AI

We know Rufus relies primarily on data within Amazon’s site and metadata, along with some off-Amazon sources such as brand websites. But what if we could train Rufus AI on a brand and the products, features, and uses directly through a new medium? LLMs are inherently designed to ingest massive amounts of information, which means brands could provide a breath of materials that extend way beyond their product pages, storefront, etc. To facilitate this, Amazon could create a new framework for brands and request information they don’t require today.

Regardless of how GEO will shape ecommerce down the line, it is already apparent how crucial investment in AI optimization is for brands who sell products online. For Amazon sellers specifically, Rufus is already driving traffic to PDPs and answering product questions. Brands must evolve their digital strategy to ensure products are visible in the important channels that customers are using for product research and discovery.