When AI Hallucinations Are Really Data Contract Failures
Short version of what I took away from this one: when a RAG assistant quotes a confidently wrong number, pull the retrieved records before you blame the model. Often the model was handed the wrong number and repeated it faithfully. A new prompt can't fix that. Neither can a new vendor. Here's the mechanism, with an invented example. An insurer's claims copilot retrieves from its policy database.…
When artificial intelligence provides confidently incorrect information, the root cause may not be the AI model itself, but rather a failure in data contract compliance. A recent article explains this phenomenon using an example of an insurer's claims assistant. The assistant retrieves policy data from a database, but after a system migration, the field naming changed from "max_coverage_limit_usd" to "per_incident_deductible_usd".
The data transformation pipeline processed the data correctly, with no errors or alerts. However, when a claims adjuster requested the coverage cap for a policy, the assistant incorrectly provided a figure of $12,500 instead of the actual $1,250,000. The error did not originate from the AI model, but from the data contract, which failed to map the correct field name to the correct data.
The article suggests several measures to prevent such errors. First, compatibility checks should be implemented in the continuous integration (CI) pipeline. Every schema consumed by an AI system should be registered, with explicit full or full-transitive compatibility settings. Renames or removals of fields read by AI consumers should go through a deprecation window.
Validators should be placed before embedding data, using tools like Pydantic for invariants such as coverage limit = deductible. A ValueError should be raised instead of an assert statement to ensure it is not stripped in optimized code. Rejecting data should trigger it to be sent to a dead-letter queue with a designated owner.
Additionally, provenance information should be attached to every data chunk, including schema version, source, and as-of timestamp. This allows for easy filtering of stale data and tracing the incident back to the exact record. The article also suggests a team rule: when a wrong answer can be traced back to bad source data, the fix should be implemented in the data contract or data pipeline, not in the system prompt.
The article emphasizes that contracts do not guarantee correct answers, but they provide a clear audit trail and help identify the source of the error.
The full article includes diagrams, sources, and a four-step audit process for detecting and resolving these issues.
Written by urgent.news from Dev.to's reporting — not their text. Machine-written — may contain errors; check the original before relying on it.
