The ultimate guide to AEO: How to get ChatGPT to recommend your product | Ethan Smith (Graphite)

About this video

• Video Title: The ultimate guide to AEO: How to get ChatGPT to recommend your product | Ethan Smith (Graphite)
• Channel: Lenny's Podcast
• Speakers: Lenny Rachitsky, Ethan Smith
• Duration: 01:11:56

Overview

This video features a conversation between Lenny Rachitsky and Ethan Smith, CEO of Graphite, about Answer Engine Optimization (AEO). They discuss how the rise of Large Language Models (LLMs) like ChatGPT is changing the landscape of search and how businesses can optimize their content to appear in AI-generated answers. The conversation covers the impact of AEO, strategies for early-stage companies, the value of AEO leads, and actionable playbooks for implementation.

Key takeaways

• AEO vs. SEO: Answer Engine Optimization (AEO) focuses on appearing in LLM-generated answers, which differ from traditional SEO where ranking a single URL is key. In AEO, being mentioned across multiple citations is often more important than being the top result.
• Early-stage Opportunities: Unlike traditional SEO, AEO offers opportunities for early-stage companies to gain visibility quickly by getting mentioned in citations like Reddit threads or YouTube videos, without needing high domain authority.
• High-Value Leads: Traffic driven by LLMs can be significantly more valuable, with Webflow reporting a 6x higher conversion rate for LLM traffic compared to Google search traffic.
• Leveraging Citations: Key strategies for AEO include optimizing content on-site (targeting topics and answering specific questions) and off-site (getting mentions in various citations like YouTube, Reddit, blogs, and established media sites).
• Reddit's Role: Reddit is a highly cited source in LLMs due to its authentic user-generated content. The recommended strategy is to participate genuinely by providing useful information from real accounts, rather than attempting to spam the platform.
• RAG vs. Core Model: AEO primarily focuses on optimizing the Retrieval-Augmented Generation (RAG) aspect of LLMs, which involves searching for and summarizing current information, rather than influencing the core training data of the model.
• Content Quality: While AI can assist in content creation, purely AI-generated content without human oversight is unlikely to perform well long-term and can lead to issues like "model collapse" or an "infinite loop of derivatives."
• The Future of Search: LLMs and traditional search engines are converging, suggesting a future where a single experience integrates aspects of both.
• Help Center Optimization: Optimizing help center content is crucial for AEO, as it can answer specific, long-tail questions that users ask LLMs, especially those related to product features, integrations, and use cases.
• Experimentation is Key: The effectiveness of SEO and AEO best practices is often overstated. It's vital to conduct experiments, use control groups, and measure results to determine what truly works for your specific situation.