Show HN: I asked 5 LLMs 10k questions and indexed every domain they cited

In a recent exploration of how large language models (LLMs) perform in answering specific queries, a new tool called CiteGEO has emerged as a significant player. This tool allows users to query multiple LLMs simultaneously, capturing their responses and the sources they cite. By indexing these responses, CiteGEO provides a clearer picture of how different models rank various brands and products across diverse categories. This initiative not only sheds light on the discrepancies between LLM outputs but also offers a structured approach to understanding model behavior and citation patterns.

Understanding CiteGEO's Functionality

CiteGEO operates by posing the same question to multiple LLMs, such as OpenAI, Anthropic, and Google, and recording the responses. For instance, when asked about the best stock screening website for Indian equities, each model generates its own shortlist of recommendations. These shortlists can vary significantly, with one model potentially ranking a brand highly while another might not mention it at all. This variability highlights a crucial aspect of LLMs: they do not generate answers based on a universal truth but rather reflect their training data and algorithms.

The tool captures not just the answers but also links each response back to the specific sentences from which the information was derived. This feature allows users to audit the citations and understand the context in which certain brands or products were mentioned. For businesses and marketers, this capability is invaluable as it enables them to track their visibility across different LLMs and identify opportunities for improvement.

The Importance of Tracking

In the context of startups, where resources are often limited, efficient tracking of engineering work and market visibility is essential. CiteGEO offers a lightweight solution that can help small teams maintain transparency and communication regarding their brand's performance in the AI landscape. By providing a detailed account of how different models perceive a brand, startups can make informed decisions about their marketing strategies and content creation efforts.

Moreover, the tool emphasizes the significance of understanding the citation landscape. For example, if a competitor's website is frequently cited by LLMs, it signals a potential opportunity for content creation or outreach. Knowing which pages are being referenced can guide businesses in crafting relevant content that aligns with what LLMs are prioritizing.

CiteGEO's approach to tracking is particularly beneficial because it does not merely provide a summary score but rather a comprehensive view of how brands are positioned across multiple models. This granularity allows for a more nuanced understanding of brand performance, highlighting where a brand might excel or lag behind in the eyes of different LLMs.

Why it matters

The implications of CiteGEO's functionality extend beyond just tracking brand performance; they touch on the broader conversation about generative AI and its role in content creation and marketing. As LLMs become increasingly integrated into search engines and consumer-facing applications, understanding how these models rank and cite information is crucial for creators and technologists.

For creators, this means that simply producing high-quality content is no longer sufficient. They must also consider how their content is perceived by LLMs and ensure that it aligns with the queries users are likely to input. This could involve optimizing content for specific keywords or topics that are frequently cited by LLMs, ultimately driving more traffic and visibility.

For technologists, the development of tools like CiteGEO represents a shift towards more data-driven approaches in understanding AI outputs. By providing a means to audit and analyze LLM responses, developers can refine their models and improve their training datasets, leading to more accurate and reliable outputs.

In conclusion, CiteGEO stands out as a pivotal tool for navigating the complexities of LLM citations and rankings. It empowers users to track their brand's visibility, understand competitive dynamics, and optimize their content strategies in an increasingly AI-driven landscape. As generative AI continues to evolve, tools that offer clarity and actionable insights will be essential for both creators and technologists alike.