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).

Let's be clear here, Google Scholar is ill designed for use for systematic reviews. I am not trying to argue otherwise.

EDIT - April 2025 Since I wrote this blog post in April 2024, "Deep Research" tools have become all the rage, that combine agentic search and producing long form reports are now all the rage. New: See updated Oct 2024 review of Undermind here!

I've watched with interest, as academic search engines use AI to improve searching.

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.

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

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

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

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.

On September 2023, OpenAI announced that ChatGPT Plus would be enhanced in three ways

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