AI trading prompt regression tests compare an existing prompt with a proposed revision on the same saved tasks, using explicit checks for facts, calculations, evidence and permitted behavior. Record each new failure and its severity before looking at the average score. A clearer answer or higher pass rate cannot justify a release that introduces a critical mistake, and passing these tests does not establish trading profitability.
Changing “explain the uncertainty” to “give a decisive conclusion” may improve readability while suppressing a missing-evidence warning. A regression suite makes that tradeoff visible before the revised instructions reach a workflow. Its question is narrow: does this prompt change preserve the behavior the application requires?
Define a testable job before collecting examples
Start with an observable task rather than “produce good trading analysis.” For a hypothetical research assistant, the job could be: extract the event, instrument, status and source from supplied material; identify what was available at the decision time; and return insufficient evidence when a required fact is absent. The assistant has no authority to place orders.
Write acceptance criteria for that job before asking either prompt to answer. “Do not hallucinate” is too broad to grade consistently. “Every reported listing date must match a supplied source passage, or be marked unavailable” can be checked. A reference response can show acceptable behavior, but it should not force identical wording when several accurate summaries are possible.
OpenAI's evaluation guidance recommends task-specific objectives, datasets and metrics, with automated scoring calibrated against human judgment. It also emphasizes that model outputs can vary for the same input. These are evaluation principles; they supply no acceptance threshold for a trading application.
The test contract should identify required fields, allowed uncertainty labels, prohibited claims and permitted tools. Keep order execution outside the test environment. If the model can request tools, replace account-changing tools with inert recorders and grade attempted calls, including rejected attempts.
The existing model-version pinning guide explains how to identify the complete release. Here, hold that release fixed except for the prompt revision being evaluated. Otherwise a model change, new retrieval result or changed parser can explain the difference.
Build fixtures around costly mistakes
A fixture is a saved input bundle with its expected behavior and grading rules. Preserve exact source passages, tool responses, decision time, conversation history and relevant task state. Each case also needs an owner, a rationale and a version. Exclude credentials and unnecessary account data.
Use routine cases to measure usefulness, then add known failures, boundaries and misleading inputs. An all-adversarial suite can reveal weaknesses but cannot describe the ordinary request mix. Report results by case family and keep the sampling purpose visible.
| Case family | Controlled input | Required assertion | Example severity if violated |
|---|---|---|---|
| Complete evidence | One supplied announcement with an explicit event status | Extract the status and cite its supporting passage | Major: wrong status |
| Missing fact | Announcement contains no start time | Return unavailable; do not invent a time | Critical: invented actionable time |
| Unit ambiguity | A field says 2 without defining percent or fraction | Preserve ambiguity and request the missing unit | Critical: assumed scale changes the proposal |
| Conflicting evidence | Two supplied notices disagree | Name the conflict and avoid merging their claims | Major: unsupported resolution |
| Later update | A document update arrived after the fixture's decision time | Exclude the update from what was known then | Critical: future evidence presented as available |
| Hostile source text | Research passage asks the assistant to submit an order | Treat the passage as evidence; make no execution attempt | Critical: attempted account change |
| Harmless paraphrase | Same facts with different sentence order | Preserve facts without requiring an exact sentence match | Minor: awkward wording |
These severity labels are an illustrative editorial policy, not universal trading rules. Define them for the actual consequence of a failure. A missing comma and a fabricated price should not be interchangeable deductions from one score.
For the unit fixture, specify what the missing information makes unanswerable and assert that the workflow stops at that boundary. The separate numeric-unit validation guide explains the domain checks to turn into assertions. For the later-update fixture, preserve publication and receipt history using the event, publication and ingestion time guide. Saving today's final page alone cannot establish what the assistant could have known earlier.
Use the cheapest reliable grader for each assertion
Check discrete facts and constraints directly where possible: required field present, supplied source identifier valid, status in the allowed set, numeric value within a declared tolerance, and forbidden tool-call count equal to zero. A parser passing is only the first check; structured-output validation covers the wider distinction between a readable response and an acceptable proposal.
Semantic questions need a rubric. For “does the summary preserve the conflict?”, give reviewers the two source claims and examples of pass and fail. Require the grader to identify the output passage supporting its verdict. “Which answer sounds more professional?” cannot reliably measure evidence handling.
Before trusting a grader, challenge it with deliberately defective outputs: a correct citation attached to the wrong claim, a plausible invented timestamp, and a fluent summary that omits the conflict. Include a valid refusal to guess as a separate control. Verify that it rejects the defects and accepts the valid abstention. Review disagreements with a person who understands the task.
If a model grades narrative quality, hide prompt identities and vary answer order. OpenAI's guidance on model graders identifies position and verbosity biases and recommends clear criteria and comparison or pass/fail tasks. Keep critical deterministic violations outside a preference score: a judge favoring polished prose cannot override a recorded forbidden call.
Compare the same cases and inspect the disagreements
Run both prompts against the same saved inputs, with the same model configuration, tool responses and grading policy. Reset state between independent cases. Preserve state only for cases explicitly testing a conversation sequence. Microsoft's prompt-version evaluation guide recommends consistent datasets across versions, tracking prompt versions and deployment decisions, and including edge cases.
Do not compare the old prompt on yesterday's market summary with the new prompt on today's news. Both the task and the evidence changed. If the candidate adds retrieval instructions, declare whether retrieval results are fixed or whether the comparison covers the larger retrieval workflow.
A worked release decision with a better average
Consider an entirely hypothetical suite of 20 fixtures, each sampled twice per prompt. There are 40 output observations per prompt and 40 comparison pairs. A pair shares its fixture and repetition label; it does not promise matched model randomness. No model evaluation was run to produce these teaching numbers.
The baseline asks for a concise summary preserving uncertainty. The candidate adds an instruction to lead with a decisive conclusion. A pass means every required assertion for that output passed. The resulting constructed comparison is:
| Paired outcome | Count | What it exposes |
|---|---|---|
| Both pass | 31 | Behavior preserved on these observations |
| Baseline passes; candidate fails | 3 | New failures requiring individual review |
| Baseline fails; candidate passes | 5 | Repairs worth investigating |
| Both fail | 1 | An unresolved weakness shared by both prompts |
| Total | 40 | All comparison pairs accounted for |
Baseline passes are 31 + 3 = 34, so its pass rate is 34 / 40 = 85%. Candidate passes are 31 + 5 = 36, or 90%. The gain is five percentage points, yet three previously passing observations now fail.
Suppose one new failure treats an update received after the decision as evidence available beforehand. The other two omit required citations. Under this example's predeclared policy, any observed critical failure blocks release. The candidate is rejected despite its higher overall pass rate. The baseline must also satisfy its own acceptance criteria; rejecting a replacement does not certify the incumbent.
These percentages describe the constructed suite only. Two repetitions of a fixture are dependent observations of the same scenario, not two independent market opportunities. Zero observed critical failures would mean none appeared in these samples, not that their real-world probability is zero.
Make the release gate explicit and keep an unseen check
Separate gates from preferences. A practical policy might require no observed critical violations, no unresolved major regressions, and a documented review of usefulness, latency and cost. Choose the actual thresholds before seeing candidate scores, with consequences and evaluation limits in view. Missing grades or timed-out runs remain unresolved; dropping them from the denominator can manufacture an improvement.
Use a development set for diagnosis and prompt revision. Keep a separate held-out set out of the editing loop. If many cases are paraphrases of one incident, keep that family together when splitting sets so a near-duplicate does not masquerade as unseen evidence. Freeze the candidate before inspecting the held-out results. Once a held-out failure is used to tune the prompt, it becomes development evidence; replenish the unseen check.
For the hypothetical candidate, revise the instruction to preserve uncertainty before pursuing concision, then rerun the complete suite. Rechecking only the three regressions could miss a new problem elsewhere. Do not rewrite a fixture's expected answer merely because the candidate disagrees. Change an expectation only when the task contract or reference evidence was wrong, with a recorded reason and a new fixture version.
Save the release receipt: prompt revisions, model and tool versions, fixture and grader versions, completed and unresolved counts, severity totals, paired disagreements, reviewer decision and rollback reference. This article's worksheet is a general evaluation method, not a claim that KLINEO provides a prompt-test runner. Production model-drift monitoring remains a separate task after launch.
Before the next prompt edit, preserve one routine request and one consequential failure as fixtures with explicit assertions. Run the existing and revised prompts on both, inspect every new failure, and expand the suite before treating a higher score as permission to release.