How to use System prompts as Ground Truth for Evaluation
Source: Dev.to
The Problem: Lack of Clear Ground Truth
Most teams struggle to evaluate their AI agents because they don’t have a well‑defined ground truth. Typical workflow:
- Spend months creating manual labels.
- Hire annotators to build datasets.
- Discover that the labels are inconsistent, expensive, and don’t scale.
The Solution: Use the System Prompt as Ground Truth
Your system prompt is the definitive source of truth for evaluation. It defines:
- The agent’s role – what it is supposed to be.
- Constraints – what it must NOT do.
- Instructions – how it should behave.
- Values – what matters to it.
Everything the agent does should be measured against these specifications.
How to Evaluate Using the System Prompt
- Extract objective criteria from the prompt.
- Automate checks that verify whether each response satisfies those criteria.
Example
System prompt:
“You are a customer support agent. You must be polite, professional, and never discuss politics.”
Evaluation questions derived from the prompt:
- Is the response polite?
- Is the response professional?
- Does the response avoid political topics?
These questions are objective because they directly reflect the instructions in the system prompt, eliminating the need for subjective labeling.
Benefits
- No expensive annotators – evaluation is automated.
- Consistent – criteria are fixed and unambiguous.
- Scalable – works for any volume of interactions.
Getting Started
Implement a framework that parses the system prompt, generates the corresponding evaluation criteria, and automatically checks each agent response against them.
This approach powers the evaluation pipeline at Noveum.ai.