What Would You Ask If No One Could Judge You? | Figma Blog (opens in new tab)

Perplexity’s founders envision it as an “answer engine” that turns web-scale information into concise, sourced explanations rather than lists of links. The product grew from a personal need for judgment-free learning and was shaped by the shortcomings of early conversational AI, especially outdated knowledge and hallucinations. Its broader goal is to make curiosity easier to express and pursue.

Building a Judgment-Free Knowledge Tool

• Aravind Srinivas was inspired by childhood “Wikipedia rabbit holes” and the evolution from printed encyclopedias to AI-powered knowledge tools.
• Perplexity aims to make learning engaging through curiosity rather than attention-grabbing entertainment.
• The company wants users to ask anything without worrying about appearing uninformed or being judged.

From Private Slackbot to Public Product

• The founders initially built a Slackbot to answer practical questions about fundraising, employee health insurance, and running a company.
• They hesitated to launch because they feared criticism for attempting to compete with Google.
• Investor Nat Friedman encouraged them to view the effort as an asymmetric bet: little downside, but potentially enormous upside.
• Perplexity launched shortly after ChatGPT, despite the founders having no previous company-building experience.

An Answer Engine with Sources

• ChatGPT highlighted problems with knowledge cutoffs, hallucinations, and unsupported answers.
• Perplexity responded by combining:
  • Natural-language interaction
  • Web search and indexing
  • Large language models
  • Inline sources and footnotes
• Its goal is to provide a direct answer while allowing users to verify the underlying information.
• Srinivas describes the product as a combination of Wikipedia and conversational chat, with information drawn from across the internet.

Making Complex Information Approachable

• Perplexity follows an 80/20 approach: identify the most important concepts and deliver most of the useful understanding quickly.
• It synthesizes information from multiple web pages into a concise explanation instead of requiring users to read extensively.
• The product aims to simplify information without reducing it to misleading or overly shallow conclusions.

Turning Answers into Further Curiosity

• Each response includes three related follow-up questions to encourage exploration.
• Srinivas argues that people are naturally curious but often lack the confidence, vocabulary, or precision to formulate good questions.
• Perplexity’s design assumes that the user is never wrong; the system should help clarify and develop a person’s curiosity rather than blame them for asking imperfectly.

Perplexity’s central recommendation is implicit in its design: make knowledge easier to access, verify, and explore, while removing the social fear that prevents people from asking questions in the first place.