Tutorial: How to Build a Structured Amazon Product Research Workflow Using Codex

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A developer tutorial published on DEV Community outlines how to use OpenAI's Codex to build a disciplined Amazon product research workflow with built-in approval gates. The system processes product candidates through hard filters — covering price range, monthly sales, review count, rating, and landed cost share — before any scoring takes place. Candidates with missing data are routed to a 'verify' queue, while those failing non-negotiable rules are outright rejected, ensuring no scoring model can override critical thresholds. Surviving candidates receive a weighted score across demand, competition, margin, and quality dimensions, with all weights and thresholds treated as versioned, auditable decisions. The tutorial emphasizes human approval checkpoints for actions that carry legal, financial, or operational risk, distinguishing safe automation from decisions requiring human oversight.

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