The challenge isn't connecting to an LLM. It's figuring out what people actually mean.

This post introduces the 'intent understanding problem' as the primary challenge in building AI-powered search, arguing that it's more critical than LLM integration or infrastructure. It details how The Fork tackles this by mapping conversational signals to searchable attributes. The post then positions Meilisearch's native 'chat route' as a solution, explaining how it parses intent, maps to the schema, generates queries, and returns results, leveraging the search engine's knowledge of the data schema. It contrasts this with building custom NLP pipelines and highlights the benefits of a native approach for conversational search.