Quality, Human-in-the-Loop, and Governance Systems
Explore how to maintain and improve large language model systems through layered quality controls including automated gates, human reviews, and governance. Understand the importance of feedback loops and operational visibility to detect failures, manage semantic drift, and ensure compliance. This lesson helps you build a data flywheel that continuously converts real-world feedback into system improvements, ensuring your deployed LLMs remain reliable and secure over time.
We'll cover the following...
In the previous lessons, we engineered a complete RAG system. It ingests data, retrieves context using hybrid search, manages conversational state, and generates answers securely. From a software perspective, the system is now deployed. From an LLMOps perspective, deployment is not the finish line; it is the kickoff of the project cycle.
Traditional software degrades when dependencies break or infrastructure ages. LLM systems degrade even when the code remains unchanged. Documentation evolves, policies are updated, terminology shifts, and users ask questions we did not anticipate. A prompt that produced perfect answers in January might start failing in March because the semantic environment might have changed. This phenomenon is known as semantic drift.
If a deployment is treated as set and forget, its quality will degrade over time. To operate an LLM system responsibly, we must build infrastructure that detects failures, captures feedback, and converts real-world mistakes into systematic improvements.
This lesson solves the problem of operational visibility. We will design the data flywheel: the set of systems that connect production usage, human judgment, automated evaluation, and governance into a continuous improvement loop.
The three layers of quality
In mature LLMOps environments, quality is maintained through multiple, layered controls, each catching a different class of ...