Citation Mapping Tools & Visualizations. Chatbots And AI Search Engines Converge: Key Strategies For SEO. A lot is happening in the world of search right now, and for many, keeping pace with these changes can be overwhelming. The rise of chatbots and AI assistants – like ChatGPT and its new model GPT-4o, along with Google’s rollout of AI Overviews and Search Generative Experience (SGE) – is blurring the lines between chatbots and search engines. New AI-first entrants, such as Perplexity and You.com, also fragment the search space. While this causes some confusion and necessitates that marketers pivot and optimize for multiple types of “engines,” it also presents a whole new array of opportunities for SEO pros to optimize for both traditional and AI-driven search engines in a new multisearch universe. This evolution raises a broader question – perhaps for another day – about redefining what we call SEO to encompass terms like Artificial Intelligence Optimization (AIO) and Generative Engine Optimization (GEO).
What Is A Chatbot Or AI Assistant? Screenshot from Wikipedia, May 2024 Search Engines. “We’re Good at Search”… Just Not the Kind That the AI era Demands - a Provocation. Recently, a librarian from a prestigious institution I met at a conference surprised me when he confessed that he and his colleagues were struggling to grasp the issues surrounding the impact of AI. But My talk helped clarify much of the fog around how to think about impact of AI on search.
His confession wasn’t an isolated one. Many librarians I speak with admit they struggle to keep up with the blizzard of new AI-powered search engines. More importantly, I sense that many of us lack the right mental models to properly discuss, analyze, and evaluate them. Here’s the uncomfortable truth: we librarians have long held a self-image as masters of search—or at least, competent practitioners. This identity creates immense pressure to stay on top of “AI search” and project understanding. The reality is that we are good at searching—just in ways that differ from what may be needed now. Our traditional strengths in search are considerable. The problem? Why this blind spot? The Sycophancy Fallacy: Why You May be Worried About the Wrong Bias with Search. I’ve had a post titled “Common Misconceptions Librarians Have About Information Retrieval” sitting in my drafts for months.
I never published it because some points felt like trivial nitpicking (lexical search doesn’t have to be Boolean; overly narrow definitions of “neural search”), and others were eventually covered elsewhere. But recently I’ve encountered two related ideas that concern me more, because they’re seductive and contain just enough truth to seem plausible—while obscuring where the genuine problems actually lie. I’m not naming sources—this isn’t about embarrassing anyone—but trust me, these aren’t strawmen arguments I made up to argue with. Buy me coffee! (via ko-fi) This argument typically starts with an accurate premise: LLMs like ChatGPT exhibit sycophantic tendencies as result of techniques like RLHF (Reinforcement Learning from Human Feedback). From there, it often goes off the rails. When you search for documents, the retrieval system isn’t “agreeing” with you. Daniel Xiao - Research Guides at Texas A&M (Detailed comparisons & suggestions)
Aaron Tay's Musings about librarianship : List of academic search engines that use Large Language models for generative answers using retrieval augmented generation (RAG) Aaron Tay's Musings about librarianship. Consensus - Evidence-Based Answers, Faster. Elicit | The AI Research Assistant. Anthropic. Andi - Next Generation Search. ChatPDF - Chat with any PDF! You.com challenges Google, Microsoft with launch of ‘multimodal conversational AI’ in search. Lexii.ai. Semantic Scholar | AI-Powered Research Tool.