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Developer Curbs LLM Hallucinations in AI Resume Tool Using Code-Level Validation

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A developer building NextStep, an AI-powered resume scoring and rewriting tool, discovered that GPT-4o produced inconsistent scores and fabricated content, prompting a reliability overhaul. To fix unstable scoring, keyword match and experience scores are now calculated deterministically in code and passed to the model only as hints it must justify overriding. For resume rewrites, the model is required to reference exact original text for every edit, making it harder to invent content without a traceable source. Final enforcement happens server-side, where each change is validated against real resume data rather than relying solely on prompt instructions. The approach also reduced API costs by introducing cache keys based on resume and job description hashes to avoid redundant model calls.

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Developer Curbs LLM Hallucinations in AI Resume Tool Using Code-Level Validation · ShortSingh