In a decisive move, Perplexity abandoned four months of work on their original project to focus entirely on the challenge of building a true answer engine for the web.

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Perplexity AI built an answer engine that combines real-time web search with large language models through a Retrieval-Augmented Generation (RAG) pipeline. The architecture uses Vespa AI for web-scale indexing and retrieval across 200 billion URLs, a model-agnostic orchestration layer that routes queries to appropriate LLMs (both proprietary Sonar models and third-party models like GPT and Claude), and a custom ROSE inference engine running on NVIDIA H100 GPUs. The system processes queries through five stages: intent parsing, live web retrieval, snippet extraction, answer generation with citations, and conversational refinement. This approach addresses AI hallucination issues by grounding responses in verifiable sources while maintaining low latency and cost efficiency through intelligent model routing and infrastructure optimization.

How Perplexity Built an AI Google

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​​Indexing and Retrieval Infrastructure

Yeah, honestly, Perplexity Pro with Deep Research is currently the AI, I use the most on a daily basis. It is shortening my online searches significantly and even summarises them well, as well as focuses on specific aspects of a topic, etc., as I wish.
As someone who had to search for decades about technical issues and doing everything manually before, having this tool at hand is a huge time &amp; headache saver.
Of course, it isn’t perfect and sometimes makes mistakes, but considering how much time it saves overall, the few mistakes I have to manually correct and research are really negligible.
It’s also nice to see them put promotional cheap first Pro years, by partnering with many other companies, so the entry into getting cheap or free Pro is also very easy.
Overall, I’m very satisfied with this tool!