Why Generic AI Hallucinates Your Schema — and How We Stop It

Confident, and wrong

A general model has seen millions of codebases but not yours. Asked how your attendance rules work, it pattern-matches to what such systems usually look like and generates plausible column names — rule_id, threshold — that don't exist in your schema. It isn't lying; it's guessing, fluently.

Fluency is the trap

The danger isn't that the answer is wrong — it's that it's wrong and well-written. On a migration, a confident-but-invented answer sends a team down the wrong path with no warning sign.

Grounding beats guessing

CodeIQ Pro answers only from your knowledge graph. Every identifier in an answer is validated against real nodes; anything invented is stripped. If the grounding is thin, the product refuses to answer rather than fill the gap with fiction.

Proven vs inferred

A hard boundary separates what's proven from what's inferred, and advisory output can never masquerade as fact. That's what makes AI trustworthy on systems where a plausible lie is the most expensive answer of all.