Quality, Human-in-the-Loop, and Governance Systems
Implement the data flywheel by building infrastructure to capture user feedback, identify hallucination hotlists, and establish human-in-the-loop workflows to refine the golden dataset 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 failure.
Layer 1: Automated gates
This layer ...