DEEP_DIVE_STORY.LNK

Perplexity’s approach combines model routing, retrieval orchestration, and grounded generation with citations to deliver fast, verifiable answers.
A recent architecture deep dive details how Perplexity blends its proprietary Sonar models with partner LLMs (e.g., GPT-4, Claude, Gemini) and routes queries via an automatic “Best” mode or explicit model selection for Pro users, optimizing for speed, reasoning depth, and output style while keeping the experience seamless for most users read the explainer.
The retrieval pipeline ranks evidence and tightly links generation to citations, yielding traceable responses and real-time relevance—an effective blueprint for RAG at scale that balances latency, cost, and quality while improving user trust through sourced outputs details here.