Designing Feedback Loops for LLM Systems in Production
Most enterprise AI systems fail because they don’t learn. This project implemented a structured feedback loop to close the gap between system output and user expectation.
Context
LLM-based knowledge systems often underperform in real-world environments. Not primarily due to model limitations, but because of a persistent gap between what the system returns and what users actually expect. In enterprise contexts, this gap is rarely measured. Feedback is unstructured, inconsistently captured, and not integrated into system improvement. As a result, systems stagnate despite continuous model iteration.
Challenges
Capturing high-quality feedback without disrupting user workflows, while ensuring privacy and usability. Designing a system that produces actionable signals rather than noise, and integrating those signals into an iterative improvement loop without increasing operational complexity.
Approach
Designed and implemented a two-step performance framework to systematically identify and reduce this gap. First, a scoring mechanism was introduced to quantify the mismatch between system responses and user expectations across multiple knowledge domains. This transformed qualitative dissatisfaction into a measurable signal. Second, a lightweight, in-product feedback mechanism was deployed, allowing users to provide free-text input directly within their workflow. To ensure scalability and privacy, feedback was aggregated and summarized using LLM-based processing, producing structured insights without exposing individual user data. These insights were then used to iteratively refine prompts and retrieval strategies, establishing a continuous learning loop at the system level.
Role
Co-designed the performance framework and feedback architecture. Worked at the interface of product, engineering, and end users to translate qualitative feedback into measurable signals and system-level improvements.
Impact
Initial iterations resulted in a consistent 10–15% improvement in response relevance across multiple knowledge managers. More importantly, the system introduced a scalable mechanism for continuous alignment between AI output and user expectations, addressing a core failure mode of enterprise AI systems.