Introduction

The continuous advancement of artificial intelligence (AI) and large language models (LLMs) has presented several opportunities for librarians to reduce their workload and become more efficient. AI chatbots previously generated responses by relying on pre-trained, large-scale data. Newer versions of chatbots have made substantial strides in their capabilities, now offering the ability to search online and supplement outputs using Retrieval-Augment Generation (RAG) to provide more accurate and relevant responses. These advancements present a promising opportunity for librarians to reduce workload and increase efficiency [1, 2].

To explore the potential of generative AI chatbots in assisting health sciences librarians with collection development, we evaluated five generative AI chatbots using two prompts designed to aid librarians. The five generative AI chatbots assessed included ChatGPT o3, NotebookLM, Google Gemini 2.5, Perplexity, and Microsoft Copilot…