Which refusal did that response hold?

Do not treat the reply as the refusal the response held, and do not ask the model which refusal it was, until you can name the refusal. Tonight, three proofs, from a log you stored. Name the refusal that response held: the response id, the content type, and the refusal text. Show a different response whose id, or whose type, is not this one. Show that another customer's log does not hold this refusal. If the log does not keep the refusal, write that. A fluent apology is not an id.

This is one refusal on one response. It is not the category a moderation call flagged. Which category did that call flag? is a moderation id, a category, a boolean, and a score. A category score can be high while this response stored no refusal, and a refusal can be stored while no moderation category was written down. It is not the stop the response carried. Name the stop the response carried is a stop reason. stop is not a refusal string. It is not the line an invoice named. Which line did that invoice name? is a line id. It is not the file a pull changed. Which file did that pull change? is a filename. A refusal is none of those.

None of what follows is a score for this product, and none of it is a promise that a benchmark's judge saw your log. The fields below are one vendor's current samples of a refusal, in two different response shapes. The samples share a sentence and do not share a field list. The rates below that are a paper about other people's prompts. A rejection rate is not the refusal you stored tonight.

OpenAI's Responses sample of a refusal prints one id and one sentence. The id is resp_1234567890. 15 characters. object is response. 8 characters. 8 is not 15. created_at is 1721596428. status is completed. 9 characters. completed_at is 1721596429. 1721596429 minus 1721596428 is 1 second. The page does not print 1 as that gap. error is null. incomplete_details is null. input is an empty array. instructions is null. max_output_tokens is null. An empty input is not the refusal sentence. model is gpt-4o-2024-08-06. 17 characters. 17 is not 15. The output array has one item. Its id is msg_1234567890. 14 characters. 15 minus 14 is 1. That 1 is a character gap, not the 1 second between the two timestamps. The page does not print the character gap. type on the item is message. 7 characters. role is assistant. 9 characters, the same length as completed, and not the same string. The content array has one object. type is refusal. 7 characters, the same length as message, and not the same string. The refusal text is I'm sorry, I cannot assist with that request. 45 characters, 8 words. The page prints neither count. input_tokens is 81. output_tokens is 11. total_tokens is 92. 81 plus 11 is 92, which the sample already stores. reasoning_tokens is 0. 0 is not 11, and 92 is not 0. Do not read the sentence as output_text because the type says refusal (OpenAI, Structured model outputs).

The chat-completions sample on the announcement is the same sentence on a different object. created is 1721596428, the same integer as created_at, and the field name is created, not created_at. The announcement does not print completed_at 1721596429. model is gpt-4o-2024-08-06 again. 17 characters, the same string. role is assistant. The refusal field holds the same 45-character sentence. The same sentence is not the same object. This sample does not print a response id, and it does not print a content type of refusal. The refusal sits on the message, beside role. finish_reason is stop. 4 characters. 4 is not 7, and stop is not refusal. stop is not completed. prompt_tokens is 81. completion_tokens is 11. total_tokens is 92. The numbers match the Responses usage, and the field names do not. prompt_tokens is not input_tokens. completion_tokens is not output_tokens. system_fingerprint is fp_3407719c7f. 13 characters. 13 is not 15, and 13 is not 14. The Responses sample does not print a fingerprint. logprobs is null. A null is not the refusal sentence. Do not paste finish_reason stop onto a Responses object that stored status completed, and do not paste resp_1234567890 onto the chat sample, which never printed it (OpenAI, Introducing Structured Outputs in the API).

The paper people quote when they want a refusal rate did not open this response. Justin Cui, Wei-Lin Chiang, Ion Stoica, and Cho-Jui Hsieh introduce OR-Bench. The benchmark is 80,000 over-refusal prompts across 10 categories, a hard subset the abstract calls around 1,000 and the conclusion calls 1,000, and 600 toxic prompts. 80,000 divided by 10 is 8,000. The page does not print 8,000 as a per-category count. They generate 2,000 toxic seeds from each category and rewrite each into 5 prompts. 2,000 times 5 is 10,000. 10,000 is not 8,000, and the page does not print 10,000. Each generation step asks for 20 prompts, and the text says 50 reduces quality. 20 is not 50. 2,000 divided by 20 is 100. The page does not print 100. They evaluate 32 models across 8 families. 32 is not 8. 32 divided by 8 is 4. The page does not print 4. Temperature on Table 2 is 0.0. Keyword matching versus GPT-4 differs by 2.4 percent for GPT-3.5-turbo-0125 and 1.2 percent for Llama-3-70b. 2.4 minus 1.2 is 1.2. The gap equals the smaller discrepancy, and the page does not print the subtraction. Table 2 is the hard subset. GPT-3.5-turbo-0301 prints 59.5 in deception and 59.5 in illegal, and overall 57.4. Two categories share 59.5. 59.5 is not 57.4. The prose says that model shows an overall rejection rate of over 57 percent on the hard set. 57.4 is over 57, and it is not the appendix's 49 percent for the same model on the 80,000. 49 is not 57.4. GPT-3.5-turbo-0613 overall is 38.4. GPT-3.5-turbo-0125 overall is 12.7. 57.4 minus 12.7 is 44.7. 38.4 minus 12.7 is 25.7. The page prints neither gap. GPT-4-turbo-2024-04-09 overall is also 12.7. The same 12.7 is not the same model. GPT-4o overall is 6.7. 6.7 is not 12.7. Claude-2.1 overall on the hard table is 99.8, and the prose says 73 percent for Claude-2.1 on the 80,000. 99.8 is not 73. Claude-3-opus prints 39.2 in the sexual column and 91.0 overall. 39.2 is not 91.0. 57.4 is not a response id, and 81 tokens are not a rejection rate (Cui, Chiang, Stoica, and Hsieh, OR-Bench).

Do the three proofs on one real refusal, in the log that actually holds it. First, the binding. Copy the response id, the content type, and the refusal text. If the type is refusal, write refusal. If the log only has finish_reason stop and a refusal field on the message, write that shape, and do not invent a content type the log did not store. Count nothing the vendor did not store. Do not infer the 45-character sentence because a sample prints it, and do not infer resp_1234567890. Second, the other response. A different id, even with the same sentence, is not this one. The Responses sample and the chat sample are that shape, and they are still samples. A table of rejection rates is not that second response. Third, the other log. A log that belongs to someone else does not contain this response id. If the only store is one shared thread, write that. A customer you cannot name is the finding.

Three moves feel like the proof and are a second problem. Deleting the response because the sentence was embarrassing, and then describing the missing row as a cleanup. The two samples share a sentence and do not share an id. Quoting 57.4 because it looks precise, while the refusal you were asked about is one object, and the table that printed 57.4 had not opened your log. Asking the model which refusal it held. The model will produce another sentence. It will not open the stored object. A category you have not kept is which category did that call flag?. Opening that score does not name this response id.

The path is ordinary when an operator can open one assistant's log without opening the next assistant's log as the way to prove it. FeedDrop is a social platform with an admin moderation panel, distinct from the next assistant. It is not a refusal ledger. No headcount, duration, or result figure belongs in that sentence. The proof of a held refusal is still the response id, the type, and the text on the log you stored.

There is a version of this afternoon where hiring the path is the wrong purchase. Nothing is in production, because no model is being called. The request was not logged. A customer has already seen another customer's data. Naming a sentence does not close a leak. The wrong value was already a write. Or no metric was ever named, so a rescue would have nothing to measure. The audit is one to two weeks: why it stalled, what is reusable, and what the fix requires. The engagement is 8–12 weeks, scoped after the audit, not before it. If the agreed metric is not hit, the build is unpaid. Rollout sits behind a human-review gate. A two-week AI readiness assessment is the earlier purchase when you do not yet have a pilot. The findings are yours. They are not a promise that 57.4 percent of safe prompts will be refused on your account.

When the call is real and the log cannot name the refusal, the missing object is specific. A stored response, in a log you administer, that shows one response id, one content type, and one refusal text, shows a different response that is not that one, and shows another customer's log without it. An AI pilot failed to reach production when the reply cannot be reconstructed as the object the API stored. The capability under that record is AI evaluation and observability. One that needs a call in order for you to see which refusal was held is telling you who holds the log. If the first slice is a fixed capability, an AI integration sprint is the shape where the acceptance sentence can include the three proofs.

Before you tell them which refusal the response held, do the three proofs and write the id. Name the response id, the type, and the text, or write that the log stored no refusal. Show a different response that is not that one, even if the sentence matches. Show another log that does not contain it. Do not paste stop onto a content type of refusal because both samples apologize, and do not paste 57.4 onto 81 because both are numbers on a page about models. The binding is the refusal you can defend.