Note: OpenAI leads the general AI space, but AI companies are developing deep research tools and experimenting with AI-powered academic searching in support of research. Perhaps you have faculty or students asking you to present these tools to classes. For more information, see Which companies are behind AI search tools?.
Perplexity is a popular AI-powered chatbot (and search or "answer" engine) that provides AI-crawled answers with citations from "trusted" web sources. Perplexity has a deep research feature, and uses large language models (LLMs) such as GPT-4 and Claude to deliver concise, accurate responses. Unlike a traditional search engine, it focuses on natural language models, understanding text and providing predictable answers in a conversational tone. It’s available via web, iOS, Android, and browser extensions, with a Pro plan for advanced features. Perplexity's CEO is Aravind Srinivas.
Perplexity uses a conversational interface that allows users to pose questions and iteratively refine searches in a dialogue-like format. The platform integrates live web search to deliver current information and provides citations to sources, which can enhance transparency and perceived credibility; however, the rationale for source selection is often unclear. In 2025, Perplexity AI became the subject of multiple lawsuits brought by major media and content organizations, including News Corp (Dow Jones and the New York Post), Reddit, and Encyclopedia Britannica. These actions allege widespread copyright infringement, unauthorized scraping of proprietary content, and trademark violations, all linked to Perplexity’s AI-driven “answer engine” model.
Bottom line: For some health sciences librarians, Perplexity may be a useful support tool when working with health professionals. At the same time, its underlying AI technologies raise concerns for those focused on scientific accuracy, transparency, and methodological rigour in evidence synthesis and review work. The platform is also facing ongoing legal challenges related to copyright infringement tied to its training data. Because information about this tool is evolving, users should consult Perplexity’s website for the most current details or discuss its use with a librarian.
More broadly, it is worth underscoring the distinction between searching for sources and searching for answers. Large language models excel at the latter, often while obscuring the former.
Presentation
Note: This presentation was selected by a librarian due to the presenter and their understanding of the product. As this is a marketing video and tutorial, some of the claims of the video should be tested and verified.
Librarian criticism
According to Angélique Roy, Canadian health sciences librarian, Queen’s University (2025), Perplexity employs "... large language models to provide comprehensive research responses (after a web search). Its inclusion of in-text citations allows users to verify information for accuracy and credibility. While several other AI-powered search engines focus on academic literature, Perplexity serves as an effective general-purpose research tool for various users. The platform bridges traditional search engines and AI assistants by offering both synthesized answers and source attribution." Also: "...Perplexity combines web searching with large language models to provide comprehensive research responses. Its inclusion of in-text citations allows users to verify information for accuracy and credibility. While several other AI-powered search engines focus on academic literature, Perplexity serves as an effective general-purpose research tool. The platform bridges traditional search engines and AI assistants by offering both synthesized answers and source attribution."
In this wiki's entry, Environmental and climate-related impacts of AI-searching, Perplexity is compared to other AI search tools in carbon dioxide emissions. Perplexity creates almost as many grams of CO2 as ChatGPT due to computational resources needed to run the platform.
"... This research evaluates the performance of platforms such as SciSpace, Elicit, ResearchRabbit, Scite.ai, Consensus, Claude.ai, ChatGPT, Google Gemini, Perplexity, and Microsoft Co-Pilot across the key stages of SLRs—planning, conducting, and reporting. While these tools significantly enhance workflow efficiency and accuracy, challenges remain, including variability in result quality, limited access to advanced features in free-tier versions, and the necessity for human oversight to validate outputs..."
Note: Please use your critical reading skills while reading entries. No warranties, implied or actual, are granted for any health or medical search or AI information obtained while using these pages. Check with your librarian for more contextual, accurate information.
