How Perplexity Built an AI Google

First, I’m blown by how RAG - often perceived as inaccurate and clunky - can be the backbone of something as sophisticated as Perplexity. Second, it’s amazing to know what it takes to build a hyper-accurate RAG pipeline. KNOWLEDGE BASE: A massive dataset built from crawling 200 billion web pages. RETRIEVAL ENGINE: Vespa, a low-latency search engine, designed to operate at web-scale, that combines vector and lexical search with ML-powered ranking. LLM: An intelligent router to run user’s query + retrieved context on the best-suited model among in-house and leading ones (GPT/Claude), balancing cost and quality.