What is Prompt Regression Test?
Prompt Regression Test is an evaluation that checks whether a prompt or related LLM application change has broken previously expected behavior.
How It Works
Prompt regression tests protect LLM applications from accidental behavior changes. A test may assert exact structured output, rubric-based quality, refusal behavior, citation presence, tool-use constraints, or latency and token budgets. Because LLM outputs can be probabilistic, tests often combine deterministic checks with semantic judges and human review for high-risk cases. The best regression suites are tied to real incidents, production feedback, and golden datasets rather than synthetic happy paths only.
Key Characteristics
- Detects behavior changes after prompt, model, retrieval, or tool updates
- Can test exact schemas, semantic quality, safety, citations, and cost limits
- Often combines deterministic assertions with LLM-as-judge or human review
- Works best when grounded in golden datasets and production failures
- Prevents prompt fixes from silently breaking other workflows
Common Use Cases
- Checking that JSON output remains schema-valid after a prompt edit
- Testing refusal behavior for unsafe requests
- Ensuring RAG answers still include citations
- Comparing a new model against current prompt expectations
- Blocking releases when known incidents reappear
Example
Loading code...Frequently Asked Questions
What does a prompt regression test catch?
It catches behavior that used to work but breaks after prompt, model, retrieval, tool, or configuration changes.
Can LLM tests be deterministic?
Some can, especially schema and policy checks. Open-ended quality tests often need rubrics, repeated runs, or judge models.
Should regression tests use production examples?
Yes, after privacy review. Real failures and user cases make tests more valuable than only synthetic examples.
How many prompt regression tests are enough?
Enough to cover critical workflows, known incidents, safety boundaries, structured outputs, and high-value user intents.