AI Prompt Regression Testing

When to Use

• Compare a prompt change with an observed baseline on a fixed evaluation set.
• Detect schema, factuality, refusal, tool-selection or task-completion regressions.
• Record a reviewable decision when results are noisy, missing or contradictory.

Workflow

1. Define the task, representative cases and forbidden outcomes. Use synthetic or approved/redacted data; freeze case IDs and the dataset revision before runs.
2. Record baseline/candidate prompt digests, exact available model/version, inference settings, tool versions and evaluation environment. Do not hardcode old model-family options or silently change models between comparisons.
3. Predeclare checks and acceptance rules. Use deterministic schema/field checks for structured output, behavioral checks for tools and a clear human/rubric standard for open text. Similarity does not prove factual or code correctness.
4. Run matched baseline/candidate cases under the same permissions and budget. Record latency/cost and repeated runs when variability matters. A timeout, missing output or judge error is incomplete, not a pass or a zero-cost result.
5. Treat judge outputs as evidence with limitations. Blind/randomize presentation where feasible, calibrate against human-labeled examples, and record judge identity/rubric. Do not ask a model to verify private reasoning or accept a judge's confidence as an objective correctness measurement.
6. Review differences case by case and report the denominators, failures and uncertainty. Keep mandatory safety/schema failures visible even if aggregate similarity improves. Use the predeclared rule, not a threshold selected after results.
7. Store a redacted report and exact artifacts at an authorized destination. Decide retain/revise/reject for the stated dataset; deployment is separate.

Regression register

Field	Type	Example / meaning
case_id	string	PTEST-001; a textual ID, not SERIAL
dataset_revision	immutable reference	exact case inventory, expected checks and labels
prompt_revision	string	baseline/candidate digest
model_runtime	string	exact available ID/version plus settings
check	string	required JSON fields or approved rubric criterion
result	enum	pass / fail / incomplete
evidence	reference	redacted actual output, tool trace or observed check
judge_configuration	optional object	exact judge ID/rubric/version and calibration limits
latency_ms	nonnegative number or null	null if not observed; never invented
cost	amount/currency or null	actual billing basis; unknown remains unknown
run_revision	immutable reference	binds the report to the actual run

IDs and statuses must have consistent SQL/JSON/CSV representations. Do not store private prompts, user messages or tokens in a public spreadsheet or Notion register.

Worked example

Two cases test a support-answer prompt. PTEST-001 requires an answer grounded in the supplied policy; PTEST-002 requires a structured escalation when the policy is absent. The candidate sounds more helpful but PTEST-002 has no observed result. The report is incomplete, not a win. The following Python 3 helper aggregates already reviewed case statuses; it does not call models or decide correctness.

def summarize_regression(results, expected_case_ids):
    if not isinstance(expected_case_ids, list) or not expected_case_ids:
        return {"status": "incomplete", "reason": "missing case inventory"}
    if any(not isinstance(case, str) or not case for case in expected_case_ids):
        return {"status": "incomplete", "reason": "invalid case IDs"}
    if len(set(expected_case_ids)) != len(expected_case_ids):
        return {"status": "incomplete", "reason": "duplicate expected case IDs"}
    if not isinstance(results, list) or any(not isinstance(row, dict) for row in results):
        return {"status": "incomplete", "reason": "invalid results"}
    by_id = {}
    for row in results:
        case = row.get("case_id")
        if not isinstance(case, str) or case not in expected_case_ids or case in by_id:
            return {"status": "incomplete", "reason": "unexpected or duplicate result"}
        if row.get("result") not in ("pass", "fail", "incomplete"):
            return {"status": "incomplete", "reason": "unknown result"}
        by_id[case] = row["result"]
    failed_cases = [case for case, result in by_id.items() if result == "fail"]
    if set(by_id) != set(expected_case_ids) or "incomplete" in by_id.values():
        return {"status": "incomplete", "reason": "missing or incomplete case",
                "failed_cases": failed_cases, "evaluated": len(by_id)}
    return {"status": "regression" if failed_cases else "checks-passed",
            "failed_cases": failed_cases, "evaluated": len(by_id)}


print(summarize_regression(
    [{"case_id": "PTEST-001", "result": "pass"}],
    ["PTEST-001", "PTEST-002"],
))

Expected output: incomplete with reason missing or incomplete case. For all observed pass statuses the output is checks-passed, meaning only those recorded checks passed; it is not a release or general quality certificate.

Limitations

• No RPC, relayer, signing key or blockchain deployment is required for this generic prompt workflow. Real integrations may have their own dependencies.
• Exact text match is unsuitable for many valid open-ended outputs; embeddings and judges have blind spots and cannot replace mandatory behavioral checks.
• A golden set must be reviewed/versioned; it is not created merely by naming it.
• This entry does not include a provider adapter, judge, dataset or runnable suite. Use the project's supplied materials and state what was actually executed.
• A finite test set cannot prove future prompt behavior or absence of injection.