A complete architecture reference for building a Perplexity-class AI search agent
Perplexity is not a smarter model. It is a disciplined Retrieval-Augmented Generation (RAG) pipeline that treats retrieval, source ranking, and inline citation as first-class engineering concerns — not afterthoughts bolted onto a chatbot. The underlying LLMs it uses (GPT-4, Claude, Gemini, its own Sonar) are the same families everyone else has access to. What differentiates it is the orchestration layer around those models.
A competing system does not require secret prompts or proprietary models. It requires robust query analysis, hybrid retrieval (BM25 + dense), multi-layer reranking, structured prompt assembly with embedded citations, constrained LLM generation, and tight observability around citation quality and latency. This is non-trivial engineering, but it is all reproducible with off-the-shelf components.