Perplexity AI: How We Built the World's Best LLM-Powered Search Engine in 6 Months, w/ Less Than $4M
TLDRThe speaker discusses the evolution of their AI search assistant, from its inception to its current capabilities. They highlight the transition from traditional search engines filled with ads and SEO content to a more intuitive, question-answering system. The assistant's development was driven by the team's own needs, and its success is measured by its utility in real-world scenarios. The speaker emphasizes the importance of iteration, user feedback, and the balance between leveraging existing models and developing their own for long-term sustainability and product improvement.
Takeaways
- 🚀 The speaker's team is focused on reimagining the future of search by creating an intuitive and impactful product for consumers.
- 🧠 The initial goal was to build a research assistant capable of answering complex questions, moving beyond traditional search engines' limitations.
- 📈 The journey was not linear; the team started with text-to-SQL, which was unrelated to consumer search, and gradually evolved their approach.
- 💼 The company was incorporated a year ago and received investment from notable figures like Elon Musk and Nat Friedman.
- 🔍 The team identified a gap in the market for an AI-powered search tool that could answer specific, complex queries that Google couldn't.
- 🤖 The development of their AI tool was driven by the team's own need for a solution to complex, enterprise-level search queries.
- 🌐 The launch of their web search feature was timed around the same period as the launch of ChatGPT, which was seen as a significant milestone in AI.
- 💡 The team learned from using their own product, iterating based on user feedback, and improving the product's speed and functionality.
- 📊 The company's strategy includes launching new features like Copilot, which offers a more interactive and dynamic search experience.
- 🔗 The platform is evolving into a comprehensive work environment with features like file uploads and collections for better collaboration and privacy.
- 🚀 The speaker emphasizes the importance of continuous improvement and the potential for their platform to become the go-to research assistant for users globally.
Q & A
What is the main goal of the company described in the transcript?
-The main goal of the company is to build the world's best research assistant that can answer any question, improving upon traditional search engines like Google by providing direct answers and complex query solutions.
How did the company initially approach the development of their search product?
-The company initially started working on text to SQL, which had nothing to do with consumer search. They aimed to explore enterprise search with LLMs and SQL, which eventually led to the development of their unique search assistant.
What challenges did the company face when they started working on their project?
-The company faced challenges such as difficulty in securing funding for search-related work, a lack of expertise in company building and enterprise search, and the complexity of understanding and searching over customized databases like HubSpot and Salesforce.
How did the company validate the usefulness of their AI research assistant?
-The company validated the usefulness of their AI research assistant by building it to answer their own questions and solve their own problems. They also received positive feedback from friends and early users who found it more useful than traditional search engines like Google.
What was the significance of launching the web search feature for the company?
-The launch of the web search feature was significant as it marked the company's entry into the consumer search market and showcased their ability to provide direct answers to complex queries, differentiating themselves from existing search engines.
How did the company improve their product after the launch of Chat GPT and the realization of its limitations?
-The company improved their product by focusing on making it fully end-to-end conversational, suggesting follow-up questions, and launching a feature called Copilot, which acts as a browsing companion and provides a more dynamic and interactive user experience.
What is the company's strategy for differentiating their product in the competitive search engine market?
-The company's strategy for differentiation includes continuous iteration and improvement of their product, focusing on providing a better user experience through speed and relevance, and expanding their product into an end-to-end platform with features like file uploads and collections for research and collaboration.
What are the technical challenges the company faced when trying to integrate various APIs and plugins?
-The technical challenges included ensuring reliability at inference time, managing latency, and orchestrating different components to provide a seamless and efficient user experience.
How does the company plan to handle the rising costs of using APIs from other entities like Open AI?
-The company plans to handle rising costs by developing and training their own models, which would give them more control over pricing and potentially reduce costs in the long term.
What are the company's thoughts on the balance between using open-source models and proprietary models?
-The company believes that while open-source models can be useful, having their own models allows for more customization and control over pricing. They see a balance between using open-source models and developing their own as the best approach for their product.
What is the company's vision for the future of their search platform?
-The company envisions their platform becoming more than just a tool, but an end-to-end platform for research and collaboration, with features that allow users to save, share, and collaborate on information efficiently and effectively.
Outlines
🚀 The Vision for the Future of Search
The speaker discusses the vision for the future of search engines, emphasizing the need for a more intuitive and user-friendly experience. They share the journey of building a research assistant that could answer any question, moving away from the traditional search engine model filled with ads and SEO content. The speaker reflects on the non-linear process of development, highlighting the challenges and the unexpected turns along the way.
🌱 Building a Product That You Use
The speaker emphasizes the importance of creating a product that the creators and their friends use. They discuss how using the product themselves provided invaluable insights and led to significant improvements. The speaker shares their personal experiences with using the product for company building tasks, such as understanding health insurance and recruiting, and how these experiences shaped the product's development.
🤖 Enhancing AI with Real-Time Data
The speaker discusses the limitations of existing AI models and the need to connect them with real-time data to provide trustworthy answers. They share the evolution of their product, from a Slack bot to a fully-fledged AI research assistant, and how it improved over time with features like focused searches on Wikipedia and Stack Overflow. The speaker also touches on the competitive landscape and how their product differentiates itself.
🛠️ Fine-Tuning and the Power of Fast Iteration
The speaker talks about the process of fine-tuning AI models and the benefits of fast iteration in product development. They share how incorporating user feedback and continuously improving the product led to the creation of Copilot, a browsing companion that enhances the user experience. The speaker also discusses the technical aspects of fine-tuning and the impact it has on user experience in terms of speed and reliability.
📚 Transitioning from a Tool to a Comprehensive Platform
The speaker describes the transition of their product from a simple tool to an end-to-end platform, with the introduction of features like collections for persistent saving and collaboration. They discuss the importance of this transition in differentiating their product and the plans for future development. The speaker also talks about the balance between being a 'rapper' and having your own models, and the decision to train their own models for better control and customization.
🌟 The Role of Open Source and Custom Models
The speaker explores the role of open source models in their product development and the decision to move towards training their own models. They discuss the benefits of controlling the pricing and customization of their models, as well as the technical challenges and innovations they've implemented to improve the speed and performance of their AI. The speaker also shares their vision for the future, including the potential of serving their own models and the continuous improvement of their product.
Mindmap
Keywords
💡Search Engine
💡Perplexity
💡Enterprise Search
💡AI Research Assistant
💡Text-to-SQL
💡Investors
💡Slack Bot
💡Discord Bot
💡Fine-tuning
💡Generative UI
💡Collections
Highlights
The speaker discusses the future of search and the creation of a world-class research assistant.
The initial challenge was the overwhelming amount of ads and SEO content in traditional search engines like Google.
The company's inception involved starting with text-to-SQL, which was unrelated to consumer search.
Investment from prominent figures like Elon Musk, Nat Friedman, and Jeff Dean marked a turning point for the company.
The focus on enterprise search with LLMs and SQL was a significant departure from mainstream search engine development.
The realization that a search engine capable of answering complex queries was a necessity led to the creation of the current product.
The importance of building a product that the creators and their friends use themselves to ensure market fit.
The transition from a Slack bot to a web search platform, showcasing the product's evolution.
The launch of Perplexity and its timing with the release of ChatGPT, which was considered a landmark moment for AI.
The integration of real-time search and summarization, enhancing the chatbot's capabilities.
The development of a conversational, multiplayer interface that allowed dynamic question and answer interactions.
The creation of a Discord bot and server, leading to positive user feedback and validation of the product's usefulness.
The strategic decision to focus on web search after the high API prices made other options unfeasible.
The introduction of Copilot as a browsing companion, marking a significant innovation in user interaction with AI.
The implementation of fine-tuning with the latest GPT models to improve speed and user experience.
The launch of Collections, a feature that transitions the product from a tool to an end-to-end platform.
The company's commitment to continuous iteration and improvement, emphasizing the importance of establishing a strong platform and infrastructure.
The exploration of training their own models to have greater control over pricing and customization.
The speaker's emphasis on the role of orchestration in combining different components to create a seamless user experience.