OpenAI Dots Custom Rules: Clear Acceptance Criteria

Clear acceptance criteria for OpenAI Dots custom rules state exactly what the rule should do, when it applies, and what a correct result looks like. Write them as observable pass-or-fail conditions, include representative examples and edge cases, and avoid relying on vague words such as “good,” “helpful,” or “appropriate.”

What “OpenAI Dots custom rules” means

The phrase is ambiguous: it does not, by itself, identify a publicly documented OpenAI product or a standard feature with a universally agreed name. “Dots” could refer to an internal tool, a team’s shorthand, or a particular interface. Therefore, the safest way to answer the question is to treat custom rules as configurable instructions or checks in a workflow and define acceptance criteria for them without assuming undocumented product behavior.

Acceptance criteria are conditions that must be satisfied before a rule or change is considered complete. They are more specific than a goal. “Make replies concise” expresses intent; “For requests asking for a definition, answer in no more than three sentences unless the user requests detail” can be evaluated against actual examples.

If “Dots” is the name of a specific system in your organization, check its own documentation for supported rule syntax, precedence, and testing features. The criteria described here concern the behavior you want, not a claim about any particular OpenAI interface.

What strong acceptance criteria include

A useful criterion establishes the situation, the expected behavior, and how to judge the result. Depending on the rule, it should also specify what must not happen. The wording should be testable by a person or an automated check, rather than depending on an unstated interpretation.

• Trigger: What user request, content, or condition activates the rule?
• Action: What should the system do when the trigger occurs?
• Boundary: When should it not apply, or when should another behavior take priority?
• Evidence: What output or observable result proves that the criterion passed?
• Exceptions: What should happen when the input is unclear, incomplete, or outside the rule’s intended scope?

Not every rule needs a long specification. A narrow formatting rule may need only a clear condition and an exact output requirement. A rule that affects safety, privacy, or consequential decisions needs more careful boundaries and review.

How to write criteria that can be tested

Start with one behavior per criterion. Combining several requirements in one sentence makes failures hard to diagnose: a response might meet the tone requirement but miss the format requirement. Split those into separate checks, then define which checks are mandatory and which are preferences.

1. Describe the intended use. Identify the kinds of requests covered, and name important requests that are outside scope.
2. Use observable language. Replace “be clear” with a measurable or reviewable condition, such as defining a technical term before using an abbreviation.
3. Specify required and prohibited behavior. State what must be included and what the rule must avoid, without creating contradictory instructions.
4. Add examples. Include a typical input, an expected outcome, and at least one near-miss or boundary case.
5. Set a review method. Decide who or what checks the result, and how disagreements or uncertain cases are handled.
6. Revisit after testing. Update the wording when examples reveal ambiguity, unintended effects, or conflicting rules.

For instance, “Use a friendly tone” is hard to score consistently. A more concrete version might say: “For routine support requests, use respectful, direct language; acknowledge the stated problem; avoid sarcasm and blame; and do not add a greeting when the user asks for output only.” This is still a judgment-based criterion, but it gives reviewers specific features to examine.

Examples of clear rules and pass conditions

Suppose a custom rule is meant to make short explanations accessible. A criterion could be: “When the user asks for a basic explanation, define any specialist term on first use and give one relevant example. If the user asks for a technical treatment, do not simplify away essential distinctions.” A test set should include a basic question, a technical question, and a prompt that does not ask for an explanation.

For a formatting rule, write a condition such as: “When the user requests a checklist, return actionable items as a list; do not add a summary paragraph if the user says ‘list only.’” Test both a normal checklist request and the explicit list-only instruction. The second case checks that the rule does not override a more specific request.

For a rule that asks the system to identify missing information, an acceptance criterion might say: “If a required input is absent and the answer depends on it, ask one focused follow-up question instead of inventing a value.” Include an example where the missing detail changes the answer and another where a reasonable general answer is still possible. This helps prevent unnecessary questions.

These examples describe desired outcomes, not guaranteed capabilities. A written rule cannot ensure perfect behavior in every case. Evaluate results across varied prompts, and treat the criteria as a way to detect and reduce failures rather than proof that failures are impossible.

Testing, conflicts, and common mistakes

A rule that passes one convenient example may still fail on reworded requests, unusual inputs, or competing instructions. Build a small, representative test set before adopting it. Include ordinary cases, boundary cases, negative cases where the rule should not activate, and examples that could be mistaken for the trigger.

• Vague standards: “Be accurate” needs a checking method and a scope. For factual outputs, specify whether sources, dates, calculations, or uncertainty must be shown.
• Overbroad triggers: A rule that applies to “every answer” can interfere with unrelated requests. Narrow it to the relevant situation.
• Conflicting instructions: Two rules may demand incompatible formats or behaviors. State priority where the system allows it, and test likely conflicts rather than assuming an order.
• Excessive detail: Long instructions can bury the requirement that matters. Keep each rule focused, and place examples next to the criterion they clarify.
• Unclear scoring: If reviewers disagree about what counts as a pass, add examples or revise the criterion before relying on the score.

When evaluating results, distinguish a rule defect from a model limitation, a missing input, or an instruction conflict. Record the prompt, expected behavior, observed behavior, and the specific criterion that failed. That gives you a concrete basis for revising the rule.

Privacy and maintenance considerations

Custom rules may shape many outputs, so avoid putting secrets, personal data, or unnecessary internal details into them. Use only the information needed to express the behavior, and follow the relevant organization’s data-handling procedures. Do not assume that a rule is private, isolated, or applied in a particular way unless the system’s documentation confirms that.

Rules also need maintenance. A criterion that fits one team’s terminology or workflow may become misleading after processes change. Assign an owner, note the intended scope, and review the rule when its underlying process or requirements change. Preserve useful test cases so revisions can be checked against earlier behavior.

Frequently Asked Questions

What is an acceptance criterion for a custom rule?
An acceptance criterion is a specific condition that shows whether the rule behaved as intended. It should be observable and testable against an example or review standard.

How many acceptance criteria should one rule have?
There is no fixed number. Use enough separate criteria to cover the trigger, expected behavior, important boundaries, and relevant exceptions without combining unrelated requirements.

Should custom rules include examples?
Yes, especially when a trigger or boundary could be interpreted in more than one way. Include examples where the rule applies and where it should not.

Can acceptance criteria guarantee a rule will always work?
No. They make expectations clearer and failures easier to find, but testing examples cannot prove that every possible input will produce the intended result.

What if two custom rules conflict?
Clarify their scope and priority if the system supports that, then test a prompt that activates both. If the result remains ambiguous, simplify or rewrite the rules rather than relying on an assumed precedence.

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