Aaron Tay

As more and more academic search tools start to increasingly leverage on the fruits of "AI" (actually transformer based models) and librarians start to encounter such tools where it is from brand new products like Elicit.com, SciSpace, Scite.ai assistant etc or from existing vendors bundling in AI such as Scopus AI, Primo Research Assistant, Statista Research AI etc (see list here), it becomes even more critical for librarians to understand how

Avoiding misconceptions - testing "AI search tools" - What not to do &amp; some research questions to consider

List of academic search engines that use Large Language models for generative answers (for the latest version - see this page)


This is a non-comprehensive list of academic search engines that use generative AI (almost always Large language models) to generate direct answers on top of list of relevant results, typically using Retrieval Augmented Generation (RAG) Techniques. We expect a lot more!

List of academic search engines that use Large Language models for generative answers and some factors to consider when using

Warning : I am not a information retrieval researcher, so take my blog post with a pinch of salt

In my last blog post, I described a simplified description of a framework for infomation retrieval from the paper -

Deep dive into AI powered embedding based search - sparse vs dense embeddings

On September 2023, OpenAI announced that ChatGPT Plus would be enhanced in three ways  1. It would allow you to speak directly with GPT and it would also be able to reply in voice  2. It would be able to create images using DALL-E 3, OpenAI's image generation model  3. It would be able to accept image inputs  Since I finally gained access to these features, I will briefly review them with my thoughts on how impactful they might be for

ChatGPT Plus - new DALL-E 3 (image creation) &amp;  Vision (image recognition) capability - A quick overview &amp; why I am disappointed.

Introduction  How do you test a tool that promises to revolutionize academic research? With AI-powered search engines popping up everywhere—from startups like Elicit.com to giants like Scopus AI—librarians, researchers, and developers need a reliable way to evaluate them.

Testing AI Academic Search Engines (1): Defining the tools

Google Scholar turned 20 last month and Nature wrote a piece with the title "Can Google Scholar survive the AI revolution?" and quoted as saying  This led me to think, how and when do I use Google Scholar vs other "AI search tools"?

Google Scholar vs other AI search tools (Undermind, Elicit, SciSpace) - how and when to use each

I recently watched a librarian give a talk about their experiments teaching prompt engineering. The librarian drawing from the academic literature on the subject (there are lots!), tried to leverage "prompt engineering principles" from one such paper to craft a prompt and used it in a Retrieval Augmented Generation (RAG) system, more specifically, Statista's brand new "research AI" feature.

Prompt engineering with Retrieval Augmented Generation systems - tread with caution!

I have been writing about what I call citation based literature mapping tools for over 5 years now, starting from Citation Gecko in 2018. This trend really intensified in 2020, with the overall victory of the open citation movement making citation linking data effectively "free", and tools like Connected papers, Researchrabbit, LitMaps  and more really started to get some traction.

All about citation chasing and tools that does citation chasing like Citation Gecko, Connected papers, Research Rabbit, LitMaps and more

Can Semantic Search be more interpretable? COLBERT, SPLADE might be the answer but is it enough?

In the last blog post , I argued that despite the advancements in AI thanks to transformer based large language models, most academic search still are focused mostly in supporting exploratory searches and do not focus on optimizing recall and in fact trade off low latency for accuracy.

Undermind.ai - a different type of AI agent style search optimized for high recall?

I've watched with interest, as academic search engines use AI to improve searching.  Elicit is probably currently the leading example of this, using transformer based language models to  improve search relevancy ranking

A conceptual view of information retrieval - can we do better with AI?

A decade ago in 2012, I observed how the dominance of Google had slowly affected how Academic databases and OPACs/ catalogues (now discovery services) work.

JSTOR generative AI pilot - Or is Semantic Search coming for academic databases?

I recently compared three academic AI search tools: Primo Research Assistant, Web of Science Research Assistant, and Scopus AI for a review article.

 Why these three?

Mainly because they are add-ons to extremely well-established academic search engines or databases:

 Primo

: Owned by Exlibris (a Clarivate company), Primo is one of the four major discovery systems used by universities, often serving as the default library search box.

The reproducibility and interpretability of academic Ai search engines like Primo Research Assistant, Web of Science Research Assistant, Scopus Ai and more

One of the earliest themes of this blog was to track tools that not only helped with discovery but also with delivery, helping users to gain access to full-text via institution subscriptions (and of course via Open Access) even if they did not start off the library homepage.

Two new browser extensions that help with delivery or access - ebsco passport and GetFTR browser extension and other developments

Synopsis (Grok 3.0 aided)
 

[A new breed of "Deep Research" tools is reshaping how we tackle complex queries, moving beyond traditional search engines and quick AI summaries. From OpenAI’s Deep Research and Google’s Gemini counterpart to academic-focused players like Elicit, Undermind.ai and SciSpace, these agentic systems promise comprehensive reports and literature reviews—albeit with a much slower response.

The Rise of Agent-Based Deep Research: Exploring OpenAI’s Deep Research, Gemini Deep Research, Perplexity Deep Research, Ai2 ScholarQA, STORM, and More in 2025

Summary (summarised by Gemini 2.0-exp)   We often think of AI-generated 'hallucinations' as blatant errors, but what happens when a RAG system produces information that's factually true but not explicitly stated in its sources?

When is a Hallucination Not a Hallucination? The Role of Implicit Knowledge in RAG

I recently gave a 1 hour talk to the Chinese American Librarians Association(CALA) Asia Pacific Chapter on my favourite topic - How "AI" is changing academic search  I have being giving many versions of this talk both internally in my institution (to librarians) and externally for almost two years and it has always been very tricky to do well.

Learning about information retrieval and "AI search" in 2025

I have a controversial and perhaps somewhat surprising (to some) view. I believe that New AI enhanced tools that help by substantially doing the literature review itself should not be used (or encouraged to be used) by undergraduates particularly freshman with little or no experience doing literature review the normal way. While this might sound like gatekeeping but I think there are real and serious reasons to consider this viewpoint.

Why use of new AI enhanced tools that help with literature review should be discouraged for undergraduates

Audio Overviews in Google NotebookLM is making waves online. When I first tried it, it was a "wow" moment for me. The last time I felt that way was trying Perplexity.ai in late 2022 and realizing that search engines could now return answers (with citations) instead of just potentially relevant links and I realized this would be a huge paradigm shift.

Audio Overviews in Google Notebook LM made me go wow....

Source  Ex Libris surprised us by suddenly releasing Primo Research Assistant to production on September 9, 2024  (when the earlier timeline was 4Q 2024 with some believing it might even be delayed). Despite the fact that there are so many RAG (retrieval augmented generation) academic search systems today that generate answers from search, this is still quite a significant event to be worth covering in my blog. Why?

Primo Research Assistant launches- a first look and some things you should know

IP and ethical issues surrounding the use of content in Large Language Models (LLMs) have sparked significant debate, but I’ve mostly stayed out of it as this isn’t my area of expertise, and while there’s much to discuss and many legal opinions to consider, ultimately, the courts will decide what’s legal. However, for those interested in exploring this topic further, I recommend Peter Schoppert’s AI &amp; Copyright substack.

AI &amp; Retrieval augmented generation search - the content problem -reactions from librarians, authors and publishers &amp; thoughts on tradeoffs

As academic search engines and databases incorporate the use of generative AI into their systems, an important concept that all librarian should grasp is that of retrieval augmented generation (RAG).   You see it in use in all sorts of "AI products" today from chatbots like Bing Copilot, to Adobe's Acrobat Ai assistant that allow you to chat with your PDF.

Retrieval Augmented Generation and academic search engines - some suggestions for system builders

Let's be clear here,

 Google Scholar is ill designed for use for systematic reviews

. I am not trying to argue otherwise. (Obligatory warning, I am not a real systematic review librarian) But why exactly?

Why EXACTLY is Google Scholar bad for evidence synthesis, systematic reviews?

I've spent a large part of my career as an academic librarian studying the question of discovery from many angles.

How learning evidence synthesis (systematic reviews etc) changed the way I search and some thoughts about semantic search as a complement search technique

Earlier related pieces - How Q&amp;A systems based on large language models (eg GPT4) will change things if they become the dominant search paradigm - 9 implications for libraries

In the ever-evolving landscape of information retrieval and library science, the emergence of large language models, particularly those based on the transformer architecture like GPT-4, has opened up a Pandora's box of possibilities and challenges.

Things I am still wondering about generative AI + Search in 2024 - impact of semantic search, generation of answers with citations and more..

I've been a subscriber of OpenAI's ChatGPT plus for a while though I have been struggling to justify it to myself for a while until recently.

GPT4+Code interpreter - Playing Electronic Resources Librarian and Research Data Management Librarian roles

Note: This is a lightly edited piece of something I wrote for my institution What is Google’s Search Generative Experience (SGE)?   In past ResearchRadar pieces, we have discussed about how search engines both general (e.g. Bing Chat, Perplexity) and academic (e.g Elicit, Scite Assistant, Scopus (upcoming)) are integrating search with generative AI (via Large Language Models) using techniques like RAG (Retrieval Augmented Generation).  But what

Google’s Search Generative Experience (SGE) is now available in 120+ countres. What you need to know

Summary

 Classification of Academic Search Tools by Skill and Performance

: This post explores a framework for categorizing academic search tools based on their skill cap (the expertise needed to use them effectively) and performance cap (the potential quality of results they can yield), drawing parallels to gaming strategies.

Are the current AI search tools that are enhanced with transformer-based models, the unicorn - low skill cap, high performance tools we are looking for?

I was asked to give a talk at CILIP Conference 2023 for the Data &amp; AI panel. I was given 10 minutes to send a recording which I eventually delivered.  But here's an extended 40-minute recording before I cut it down. As I was just practicing, the delivery might not be the best, and in fact the final 10-minute version might be better!

Recorded extended talk - The possible impact of AI on search and discovery​

One of the tricks about using the newer "AI powered" search systems like Elicit, SciSpace and even JSTOR experiment search is that they recommend that you type in your query or what you want in full natural language and not keyword search style (where you drop the stop words) for better results. So for example do

Why entering your query in natural question leads to better result than keyword searching with the latest AI powered (Dense retrieval/embedding models) search

As an academic librarian, I’m often asked: “Which AI search tool should I use?” With a flood of new tools powered by large language models (LLMs) entering the academic search space, answering that question is increasingly complex. Both startups and established vendors are rushing to offer “Deep Search” or “Deep Research” solutions, leading to a surge in requests to test these products.

Ai2 Paper Finder and Futurehouse PaperQA2: More transparent Deep Search for Scholars?

Following my recent talk for the Boston Library Consortium, many of you expressed a strong interest in learning how to test the new generation of AI-powered academic search tools. Specifically, evaluating systems using Retrieval-Augmented Generation (RAG) was the top request, surpassing interest in learning more about semantic search or LLMs alone. This is a crucial topic, as these tools are rapidly entering our landscape.

Aaron Tay's Musings about librarianship

GPT4+Code interpreter - Playing Electronic Resources Librarian and Research Data Management Librarian roles

Recorded extended talk - The possible impact of AI on search and discovery​

Recorded extended talk - The possible impact of AI on search and discovery