First, in order for you dependable solutions about your corporation, the mannequin has to see your corporation. That begins with retrieval-augmented technology (RAG) that feeds the mannequin the appropriate slices of knowledge and metadata—DDL, schema diagrams, DBT fashions, even a number 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 scale back unfamiliarity for the fashions.
Second, most AI apps are amnesiacs. They begin contemporary 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 scale 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 perform/device calling—not simply prose—so the mannequin asks for get_metric('active_users', date_range="Q2") reasonably than guessing desk names. The extra you deal with the mannequin like a planner utilizing dependable constructing blocks, the much less it hallucinates.
