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Developer Tackles AI Hallucinations in School Codes Using Real-Time API Validation

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A developer discovered that AI models were generating inaccurate or entirely fictitious school organization codes, a problem rooted in the flawed training data the models relied on. The issue, encountered while building a tool dependent on standardized Korean school codes, caused the AI to confidently produce non-existent identifiers. An initial attempt to fix the problem by pre-fetching a static list of codes from an official API proved insufficient due to inconsistent response formats and missing entries. The developer ultimately resolved the hallucinations by implementing a real-time query function that retrieves and validates standard school codes from the Nice official open API during answer generation. The case highlights that grounding AI outputs in live, authoritative data sources is often necessary to prevent confident but incorrect responses.

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