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.
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. Its CEO is Aravind Srinivas.
Unlike other search engines, Perplexity uses a conversational interface, allowing users to ask questions and refine searches in a dialogue-like manner. The platform integrates web search to provide up-to-date information and includes citations to sources, enhancing transparency and credibility, though selection of sources lacks clarity. In 2025, Perplexity AI incurred multiple lawsuits from major media and content companies, including News Corp (Dow Jones and the New York Post), Reddit, Encyclopedia Britannica, and others. The core allegations are widespread copyright infringement, unauthorized content scraping, and trademark violations, stemming from Perplexity's AI "answer engine" model.
Bottom line: For some health sciences librarians, Perplexity might support their work with health professionals but its underlying AI technologies raises concerns for those interested in scientific accuracy, transparency and rigour in performing reviews. It is also facing several lawsuits due to copyright infringement based on its underlying training data. Information provided to you on this page is changing, so check the tool's website for current information (or discuss with a librarian). I like to elucidate the distinction between searching for sources and searching for answers. This much is true: LLMs provide the second while hiding the first.
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.
