Retailer's LLM inventory recommender cuts hallucinations from 15% to 1.5%

On October 17, 2026, InfoQ Chinese published an article titled "A Production-Grade LLM Platform Engineering Handbook," describing how a large retail company's inventory accuracy platform brought down the hallucination rate of its LLM-based inventory recommendation system. According to the account, by the end of the first month in production, roughly 15% of the agent's returned results were hallucinations—statements that sounded confident but had no factual basis. Six months later, that figure had fallen to 1.5%.

The article attributes the drop not to a stronger foundation model, but to an architectural decision: the team stopped treating LLM capabilities as an application-layer business problem and instead treated them as platform infrastructure. The platform was built jointly by business, product, software engineering, data science, and data engineering staff, and now serves dozens of application teams across inventory audits, replenishment reviews, and discrepancy triage. It provides unified routing and governance for foundation models including Gemini and GPT. The system analyzes anomaly signals across millions of SKUs and billions of historical inventory records to generate inventory correction suggestions for business users.

The article also notes that all the foundational components it describes are implemented in an accompanying GitHub repository. The code is deliberately designed with no hidden abstraction layers, exposing internal workings so the engineering patterns remain clear and understandable. One deliberate difference exists between the repository and the production environment. The report does not name the retail company or provide further details such as how the hallucination rate was measured.