Where Prompts Belong in an Evaluated System
Explore how treating prompts as software artifacts within evaluated AI systems enhances traceability and evaluation rigor. Understand the challenges of prompt management, the benefits of version control, and how to prevent failures caused by disconnects between prompt iteration and system behavior. This lesson helps ensure prompt changes are trackable, reviewed, and integrated to maintain system reliability and ease debugging.
Early in development, prompt changes tend to feel low-effort. Teams tweak wording, rerun a few examples, and move on. Once evaluation becomes systematic, with traces under review, failures categorized, and behavior protected over time, prompt changes stop being casual. They become some of the highest-leverage and highest-risk changes in the system.
This is where many teams lose rigor, and prompts drift away from the rest of the system. They reside in dashboards, admin panels, notebooks, or shared documents, while evaluation findings are stored elsewhere. When a failure appears in a trace, it becomes hard to answer basic questions: which prompt version caused this, who changed it, and whether the fix you discussed actually shipped. This lesson focuses on that breakdown and on how to treat prompts as part of the evaluable system, not as isolated text.
Why do prompts become a bottleneck once evaluation starts working?
Once you start reviewing traces regularly and running evaluations against real failures, prompt changes accelerate. A single failure may trigger several iterations, such as tightening an instruction, adding a constraint, clarifying refusal behavior, or restructuring the context. At this stage, many teams discover that they can no longer answer basic questions, such as which prompt produced a given trace, ...