Fixing LLM hallucinations and order-flow errors for Instagram DM commerce

Executive Summary

Merchants using Instagram and Messenger rely on LLMs for customer chats but face hallucinations, wrong prices, and broken order flows. Build a grounded, schema-driven conversational layer that enforces inventory and structured order capture. RAG and vector stores are mature enough to support low-latency grounding, and many merchants now sell primarily through Instagram DMs and Messenger, increasing chat volume and lost-revenue risk from hallucinations. The source describes active use of Meta webhooks and an LLM, showing adoption and an immediate pain. Increased API access to LLMs and widespread availability of product catalogs via platforms like Shopify make building a deterministic middleware for conversational commerce feasible and valuable today. Provide a conversation runtime that enforces deterministic business rules per merchant, combining a grounding layer (inventory/price API + RAG with canonical product schemas), a schema-driven dialog manager for required fields (name, phone, location), and transactional safeguards so the agent cannot confirm orders without a validated structured payload. Differentiate by capturing conversion and dispute signals per merchant to build an anonymized feedback moat that improves grounding accuracy over time. Source evidence: the OP already passes conversations to an LLM and handles Meta webhooks, so a middleware that validates and patches model outputs at the API and schema level is a direct wedge.