Engineer Shares How He Built a GPT-4 Pipeline Scoring 10,000 Jobs Daily

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A software engineer publicly detailed the architecture behind an AI-powered job scoring pipeline capable of processing over 10,000 listings per day using GPT-4. An initial naive approach — feeding full job descriptions directly into prompts — failed at scale due to high latency, unpredictable costs, and inconsistent scoring results. The rebuilt system introduced GPT-4 function calling with a strict JSON schema, forcing the model to return structured, predictable output that could be stored and queried directly in PostgreSQL. A pre-filtering stage using keyword and rule-based logic was added before any LLM call, reducing unnecessary API usage and keeping costs manageable. The engineer shared the experience as a practical guide for developers looking to deploy large language models reliably in production data pipelines.

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ProgrammingDEV Community ·

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