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First, if you need dependable solutions about your small business, the mannequin has to see your small business. That begins with retrieval-augmented generation (RAG) that feeds the mannequin the fitting slices of information and metadata—DDL, schema diagrams, DBT fashions, even a couple of consultant row samples—earlier than it solutions. For text-to-SQL particularly, embody desk/column descriptions, lineage notes, and identified be part of keys. Retrieval ought to embody ruled sources (catalogs, metric shops, lineage graphs), not only a vector soup of PDFs. Spider 2.0’s outcomes make a easy level that when fashions face unfamiliar schemas, they guess. So, we have to cut back unfamiliarity for the fashions.
Second, most AI apps are amnesiacs. They begin recent every request, unaware of what got here earlier than. You thus want so as to add layered reminiscence (working, long-term, and episodic reminiscence). The center of this reminiscence is the database. Databases, particularly ones that may retailer embeddings, metadata, and occasion logs, have gotten crucial to AI’s “thoughts.” Reminiscence elevates the mannequin from pattern-matching to context-carrying.
Third, free-form textual content invitations ambiguity; structured interfaces cut back it. For text-to-SQL, think about emitting an summary syntax tree (AST) or a restricted SQL dialect that your execution layer validates and expands. Snap queries to identified dimensions/measures in your semantic layer. Use operate/instrument calling—not simply prose—so the mannequin asks for get_metric('active_users', date_range="Q2") somewhat than guessing desk names. The extra you deal with the mannequin like a planner utilizing dependable constructing blocks, the much less it hallucinates.