Google's Search Stack Lessons for LLM Systems

Google's early search stack separated crawling, indexing, and ranking into modular stages, pushing heavy computation offline. That architecture now shapes modern LLM systems, where retrieval, embedding, caching, and inference face similar latency and scalability constraints. Precomputation saves latency but risks staleness, a trade-off still defining AI infrastructure.