Teaching the model: Designing LLM feedback loops that get smarter over time

Large language models (LLMs) have dazzled with their ability to reason, generate and automate, but what separates a compelling demo from a lasting product isn’t just the model’s initial performance. It’s how well the system learns from real users.

Feedback loops are the missing layer in most AI deployments. As LLMs are integrated into everything from chatbots to research assistants to ecommerce advisors, the real differentiator lies not in better prompts or faster APIs, but in how effectively systems collect, structure and act on user feedback. Whether it’s a thumbs down, a correction or an abandoned session, every interaction is data — and every product has the opportunity to improve with it.

This article explores the practical, architectural and strategic considerations behind building LLM feedback loops. Drawing from real-world product deployments and internal tooling, we’ll dig into how to close the loop between user behavior and ...