Production Prompt Checklist

Prompt / feature: _______________ Owner: _______________ Date: _______

1. The task#

• [ ] The prompt states one task, not three
• [ ] Success is defined, you can say what a correct output looks like
• [ ] The audience and register are stated where they matter
• [ ] Length constrained
• [ ] Examples included where the task shape is unusual

Examples do more work than instructions for anything with a specific output shape. Two good ones usually beat a paragraph of description.

2. Untrusted content#

• [ ] Retrieved or user-supplied content is clearly delimited as data
• [ ] The prompt states that content inside the delimiters is not an instruction
• [ ] No user content is concatenated directly into the instruction section
• [ ] Nothing the model reads can directly trigger a tool with real effect

Any text your system reads can contain instructions aimed at it. In a chatbot that produces a bad answer; in a system with tools it produces an action. Delimiting is the minimum, not the solution, see AI agents for the architectural part.

3. Refusal and uncertainty#

• [ ] There is an explicit instruction on what to do when the answer is not available
• [ ] The exact refusal wording is written down
• [ ] Refusal behaviour is tested on questions known to be unanswerable
• [ ] Over-refusal measured too

The refusal text: _______________

A system that always answers is not confident, it is unmeasured.

4. Output the calling code can rely on#

• [ ] Output format specified precisely, schema, fields, types
• [ ] Structured output enforced by the API where available, not requested politely
• [ ] The parser handles a malformed response without crashing
• [ ] Behaviour defined for an empty or truncated response
• [ ] No secrets, internal identifiers or system details echoed back to users

5. Configuration#

• [ ] Model and version pinned. Not a floating "latest" alias
• [ ] Temperature and sampling settings deliberate, and recorded
• [ ] Token limit generous enough to complete the task
• [ ] Timeout and retry policy set
• [ ] Cost per call measured

Pinning matters more than it appears. A provider updating a model underneath you changes your system's behaviour with no change on your side, and the symptom is a quality complaint nobody can reproduce.

6. Version control and testing#

• [ ] The prompt lives in version control, not in a database field or a console
• [ ] Changes go through review like code
• [ ] A test set of at least 20 real cases exists, with expected outcomes
• [ ] The suite is re-run before any prompt change ships
• [ ] Pass rate recorded per run, with the number of runs
• [ ] Every reported bad output is added to the test set permanently

A prompt edited directly in production, with no test set, is an untracked change to system behaviour. It is the least controlled thing in most AI deployments.

7. Safety and compliance#

• [ ] Disclosure that the user is interacting with AI, where required
• [ ] Generated content labelled where required
• [ ] Guidance on what the system must not discuss or do
• [ ] Personal data handling consistent with policy
• [ ] Logging excludes sensitive content, or is retained appropriately

8. Before shipping#

• [ ] Run against the test set; result recorded
• [ ] Run several times on the same input, variation observed and acceptable
• [ ] Tried deliberately awkward inputs: empty, very long, wrong language, adversarial
• [ ] Cost and latency at expected volume checked
• [ ] Someone other than the author has read the prompt

Sign-off#

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