LLM Query Analysis Transforms AI Search Product Insights

This guide explains how to build an end-to-end LLM query analysis practice for AI search, chatbots, and assistants, covering data collection, intent taxonomies, architectures, and metrics. It cites a VLDB 2025 survey cataloging 210 studies (70% in the last four years) and a McKinsey case that cut mean time-to-insight from three weeks to under 48 hours and uncovered a 13% revenue opportunity. The guide details lifecycle stages, normalization, enrichment, and intent classification to turn queries into product and content priorities.