Perplexity AI: How The Answer Engine Builds Itself

•5 min read
product developmentcustomer experienceaisearch enginesmachine learninginternal tools

In the competitive landscape of AI-driven tools, the practice of a company using its own software is a powerful testament to its utility and a critical driver of its evolution. For Perplexity AI, the company behind the increasingly popular "answer engine," this internal use is not just a policy but a core part of its development philosophy. By leveraging their own conversational search technology in their daily workflows, the team at Perplexity is not only refining their product but also fundamentally shaping the future of how we access information.

At its heart, Perplexity's mission is to provide direct, accurate, and sourced answers to user queries, moving beyond the traditional list of blue links. To achieve this, their engineering team has built a sophisticated system that orchestrates multiple large language models (LLMs), including their own proprietary models, in concert with a robust search infrastructure. A key aspect of their development process, as highlighted in a report by ZenML's LLMOps Database, is "regular dogfooding of the product." This hands-on approach allows the team to experience the product just as a user would, leading to rapid identification of issues and a more intuitive understanding of where improvements are needed.

From the Inside Out: Building a Better Search Experience

The internal use of Perplexity AI informs everything from the user interface to the core algorithms. When an engineer at Perplexity has a question, they don't turn to a traditional search engine; they turn to their own creation. This constant interaction provides immediate feedback on the quality of answers, the relevance of sources, and the overall conversational flow.

This iterative process is crucial for refining features like Pro Search, which is designed to handle complex, multi-step queries. As an engineer at Perplexity, William Zhang, shared with LangChain, "It's harder for models to follow the instructions of really complex prompts. Much of the iteration involves asking queries after each prompt change and checking that not only the output made sense, but that the intermediate steps were sensible as well."1 This demonstrates a meticulous approach to quality, driven by the team's own expert usage.

The benefits of this internal feedback loop are evident in several key areas of the product:

  • Refined Reasoning: By constantly testing the limits of their own system with complex questions, the Perplexity team can fine-tune the planning and execution steps their AI takes to arrive at an answer. This leads to more coherent and logically structured responses for all users.
  • Enhanced Source Curation: A core tenet of Perplexity is providing transparent and reliable sources. Internal use helps in identifying and prioritizing high-quality sources, ensuring that the answers are not only accurate but also trustworthy.
  • Improved User Interface: Experiencing the product daily allows developers and designers to identify and smooth out any friction points in the user journey, from asking a question to exploring the provided sources.

Navigating the Challenges of Self-Reliance

While building on one's own technology is a powerful development strategy, it's not without its potential pitfalls. The very act of being an expert user can sometimes create a blind spot to the experience of a novice. A feature that seems intuitive to the creators might be confusing to a newcomer. Perplexity seems to mitigate this through a strong focus on user feedback and continuous evaluation, as noted in the ZenML report.

More significant challenges lie in the broader operational and strategic realms. A recent investigation by Appknox highlighted several security vulnerabilities in Perplexity's Android app. While not directly a result of their internal usage model, it underscores the immense responsibility that comes with building and promoting a tool designed for widespread information access. The internal team's focus on functionality could, if not carefully balanced, overshadow the critical need for robust security.

Furthermore, Perplexity's strategic shift towards an advertising-based model, as reported by AutoGPT, introduces a new set of considerations. The internal culture, which has been focused on delivering the best, most unadulterated answers, will need to navigate the complexities of integrating sponsored content without compromising the user trust they have worked so hard to build. The risk of the "enshitification" engine, as one Hacker News commenter put it, is a real concern for any platform moving towards an ad-supported future.

The Path Forward: A Double-Edged Sword

Perplexity AI's commitment to using its own product is undoubtedly a core reason for its rapid ascent and the high quality of its answer engine. This practice fosters a deep understanding of the product's strengths and weaknesses, leading to a more refined and user-centric tool. As Aravind Srinivas, CEO and co-founder of Perplexity, has alluded to, this internal validation is a crucial step before rolling out new features to the public.

However, the journey ahead will require a delicate balance. The team must continue to leverage its internal expertise to innovate while actively seeking and incorporating feedback from a diverse user base to avoid developing in a vacuum. The challenges of security and the introduction of advertising will test the company's commitment to its founding principles of accuracy and trustworthiness.

Ultimately, Perplexity's story is a compelling case study in the power of a company believing in its own creation. By being their own most demanding users, they are not only building a better product but are also at the forefront of defining a new paradigm for how we interact with and find knowledge in the digital age. The path they forge will be a valuable lesson for the entire software industry.