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

MeiliSearch is an open-source search engine that provides fast and relevant search capabilities for developers. Readers can learn about search algorithms, indexing techniques, and building search functionality into their applications. With documentation, tutorials, and community support, MeiliSearch empowers developers to enhance the search experience for their users.

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Intent understanding — translating natural language queries into structured search parameters — is the hardest unsolved problem in AI-powered search, not LLM integration. Using The Fork's restaurant discovery app as a case study, the post illustrates how queries like 'Italian restaurant near Bastille for under 30€ with outdoor seating' must be decomposed into discrete filters (cuisine, location, price, amenity) that a search engine can act on. Implicit signals like 'quiet' or 'anniversary-appropriate' don't map to database fields, requiring a sophisticated interpretation layer. Most teams building conversational search reinvent the same wheel: entity extraction, filter mapping, ambiguity resolution, and validation. Meilisearch argues that because a search engine already knows the data schema, intent parsing is best handled natively at the engine level rather than as a custom middleware layer built by each team.

Why intent understanding is the hardest part of AI search

The gap between natural language and structured queries

Why building this from scratch is painful

How Meilisearch solves intent understanding

Why this matters for the AI search ecosystem

<p>Completely agree — extracting intent is harder than generating answers. Mapping vague words like “quiet” or “romantic” to real filters is where most search systems break down, so handling it closer to the search engine makes a lot of sense.</p>
