Connector Depth Is an AI Accuracy Problem

Reliable AI-driven data access depends less on the number of supported sources than on the depth of metadata each connector provides, as simple table-and-column schemas can lead language models to generate plausible but semantically incorrect queries. Using a Salesforce opportunity query as an example, the text contrasts a basic connector that incorrectly joins opportunity owners to contacts with Connect AI’s metadata-rich approach, which identifies correct relationships, uses source-specific fields such as IsClosed, and accounts for tenant-specific values. It argues that AI systems require relationship mappings, field constraints, source-specific query behavior, pagination and rate-limit handling, and annotations for computed or virtual fields to retrieve complete and accurate data. Connect AI is presented as separating the LLM’s role in determining what data to retrieve from the connector’s responsibility for retrieving it correctly, including across more than 350 sources and cross-system queries without data replication. The text recommends evaluating connectors through tests involving joins, picklists, source semantics, pagination, and schema variation between tenants, concluding that deeper metadata rather than improved prompting is essential to reducing silent errors in AI-generated data queries.

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