llama3.1

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1. 3d
   Retail LLM platform cut agent hallucinations from 15% to 1.5% in six months

   An InfoQ China engineering playbook describes a large retailer's LLM-based inventory recommendation system, where about 15% of agent responses were hallucinations at the end of the first production month; six months later the rate had fallen to 1.5%. The authors say the drop came not from a stronger base model but from treating LLM capabilities as platform infrastructure: shared retry and failure classification, a prompt registry with versioning and rollback, schema enforcement at the platform boundary, per-request token cost attribution, behavioral observability and tool routing. The platform now serves dozens of application teams across inventory audit, replenishment review and discrepancy triage, and is built on Google's ADK orchestrator, LiteLLM for routing to models such as Gemini and GPT, and MCP-based tool services, with a companion codebase that runs locally on llama3.1 via Ollama to avoid API costs.

   InfoQ 中文 AI&大模型 · 🔥 0

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