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Teaching the model: Designing LLM feedback loops that get smarter over time
How to close the loop between user behavior and LLM performance, and why human-in-the-loop systems are still essential in the age of gen AI.
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. Drawing from real-world product deployments and internal tooling, we’ll dig into how to close the loop between user behavior and model performance, and why human-in-the-loop systems are still essential in the age of generative AI. In internal applications, we’ve used Google Docs-style inline commenting in custom dashboards to annotate model replies, a pattern inspired by tools like Notion AI or Grammarly, which rely heavily on embedded feedback interactions.
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