Shopping Research is a feature in ChatGPT that acts as a personalized shopping assistant.
Simply describe what you’re looking for, such as “a lightweight laptop for travel”, and ChatGPT gathers product details, reviews, specs, prices, and comparisons from the web.

You can refine the results by marking products as “Not interested” or “More like this”, helping ChatGPT understand your preferences.

At the end, you receive a custom buyer’s guide that explains the pros, cons, and trade-offs of each option, making your purchase process easier and more informed.

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Generative Engine Optimization (GEO), also known as Large Language Model Optimization (LLMO), is the process of optimizing content to increase its visibility and relevance within AI-generated responses from tools like ChatGPT, Gemini, or Perplexity.

Unlike traditional SEO, which targets search engine rankings, GEO focuses on how large language models interpret, prioritize, and present information to users in conversational outputs. The goal is to influence how and when content appears in AI-driven answers.

Yes! RankWit includes unlimited country tracking across all plans at no additional cost.
You can monitor AI visibility for any market worldwide.

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RankWit analyzes your existing content and gives actionable, data-backed recommendations for improving your AI visibility. Suggestions include:

• Rewriting sentences to be more concise and AI-parsable
• Restructuring content into formats AI engines prefer (e.g., lists, FAQs, summaries)
• Highlighting authority signals, such as including stats, sources, or clear claims
  These optimizations are designed to increase the chances that AI platforms surface your content over competitors’.

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Credits determine how much AI tracking you perform.
A single credit = 1 prompt × 1 AI model.

For example:

• 10 prompts
• × 3 AI models (ChatGPT, Google AI Overview, Perplexity)
  = 30 credits

This transparent system ensures you only pay for the tracking you use.

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RankWit gives you a complete picture of how your brand appears across major AI platforms.
We run structured prompts through leading AI systems (including ChatGPT, Google AI Overview, and Perplexity) and then evaluate the responses for:

• Brand mentions
• Sentiment
• Ranking or positioning
• Competitor visibility
• Opportunities and risks

This analysis helps you understand exactly how AI systems perceive and present your brand.

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Setup takes only a few minutes.
Just add your website, configure your prompts and RankWit begins analyzing your AI visibility immediately.

Large Language Models (LLMs) are AI systems trained on massive amounts of text data, from websites to books, to understand and generate language.

They use deep learning algorithms, specifically transformer architectures, to model the structure and meaning of language.

LLMs don't "know" facts in the way humans do. Instead, they predict the next word in a sequence using probabilities, based on the context of everything that came before it. This ability enables them to produce fluent and relevant responses across countless topics.

For a deeper look at the mechanics, check out our full blog post: How Large Language Models Work.

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GEO requires a shift in strategy from traditional SEO. Instead of focusing solely on how search engines crawl and rank pages, Generative Engine Optimization (GEO) focuses on how Large Language Models (LLMs) like ChatGPT, Gemini, or Claude understand, retrieve, and reproduce information in their answers.

To make this easier to implement, we can apply the three classic pillars of SEO—Semantic, Technical, and Authority/Links—reinterpreted through the lens of GEO.

1. Semantic Optimization (Text & Content Layer)

This refers to the language, structure, and clarity of the content itself—what you write and how you write it.

🧠 GEO Tactics:

• Conversational Clarity: Use natural, question-answer formats that match how users interact with LLMs.
• RAG-Friendly Layouts: Structure content so that models using Retrieval-Augmented Generation can easily locate and summarize it.
• Authoritative Tone: Avoid vague or overly promotional language—LLMs favor clear, factual statements.
• Structured Headers: Use H2s and H3s to define sections. LLMs rely heavily on this hierarchy for context segmentation.

🔍 Compared to Traditional SEO:

• ✅ Similarity: Both value clarity, keyword-rich subheadings, and topic coverage.
• ❌ Difference: GEO prioritizes contextual relevance and direct answers over keyword stuffing or search volume targeting.

2. Technical Optimization

This pillar deals with how your content is coded, delivered, and accessed—not just by humans, but by AI models too.

⚙️ GEO Tactics:

• Structured Data (Schema Markup): Clearly define entities and relationships so LLMs can understand context.
• Crawlability & Load Time: Still important, especially when LLMs like ChatGPT or Perplexity use live browsing.
• Model-Friendly Formats: Prefer clean HTML, markdown, or plaintext—avoid heavy JavaScript that can block content visibility.
• Zero-Click Readiness: Craft summaries and paragraphs that can stand alone, knowing the user may never visit your site.

🔍 Compared to Traditional SEO:

• ✅ Similarity: Both benefit from clean code, fast performance, and schema markup.
• ❌ Difference: GEO focuses on how readable and usable your content is for AI, not just browsers.

3. Authority & Link Strategy

This refers to the signals of trust that tell a model—or a search engine—that your content is reliable.

🔗 GEO Tactics:

• Credible Sources: Reference reliable, third-party data (.gov, .edu, research papers). LLMs often echo content from trusted domains.
• Internal Linking: Connect related content pieces to help LLMs understand topic depth and relationships.
• Brand Mentions: Even unlinked brand citations across the web may boost your perceived credibility in LLMs’ training and inference models.

🔍 Compared to Traditional SEO:

• ✅ Similarity: Both reward strong domain reputation and high-quality references.
• ❌ Difference: GEO may rely more on accuracy and perceived authority across training data than on backlink volume or anchor text.

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Generative Engine Optimization (GEO) and Answer Engine Optimization (AEO) are closely related strategies, but they serve different purposes in how content is discovered and used by AI technologies.

• AEO is focused on helping your content become the direct answer to user queries in AI-powered answer engines like Google's SGE (Search Generative Experience), Bing, or voice assistants. It emphasizes clear formatting, Q&A structure, and schema markup so that AI systems can easily extract and present your content in snippets or spoken responses.
• GEO, on the other hand, is a broader approach designed to ensure your content is used, synthesized, or cited by generative AI models like ChatGPT, Gemini, Claude, and Perplexity. It involves creating high-quality, authoritative content that large language models (LLMs) recognize as trustworthy and relevant. It may also include using metadata tools (like llms.txt) to guide how AI systems interpret and prioritize your content.

In short:
AEO helps you be the answer in AI search results. GEO helps you be the source that generative AI platforms trust and cite.

Together, these strategies are essential for maximizing visibility in an AI-first search landscape.

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