Which input did that response send?

A customer says the model answered the wrong question, or says the reply quoted a file the product never sent. Do not treat the reply as the input the response sent, and do not ask the model which input it was, until you can name the input item id, the type, and the text, show a different item whose id or type is not this one, and show that another response's list does not hold it. If the request stored a string and no item id, write that. If any of the three needs a log you cannot open, you have not named the input. You have a reply and a guess.

This is an input item on a response. It is not the prompt the request stored as system text. Which prompt did that request send? is the system text, the messages, and the tools. An input item can exist while that system field is absent, and a system string is not an item id. It is not the search the response called. Which search did that response call? is a web-search call id. A search call is an output item, not the input that preceded it. It is not the step a run took. Which step did that run actually take? is a step id. It is not the transfer a charge created. Which transfer did that charge create? is a transfer id. It is not the protection a branch set. Which protection did that branch set? is a review count. An input item is none of those.

None of what follows is a score for this product, and none of it is legal advice. The fields below are one vendor's current samples of an input item and of requests that never received an item id. The samples that share a role do not share an id. The figures below that are a paper's attack averages, not the text on your log. An average across other people's tasks is not the input this response sent.

OpenAI's list of input items prints two examples, and they are not one item. The schema example has data length 1. The item id is the string id. 2 characters. type is input_text on the content, 10 characters, and type is message on the item, 7 characters. 10 is not 7. The text is the string text. 4 characters. role is user. 4 characters, the same length as the text, and not the same string. status is in_progress. 11 characters. prompt_cache_breakpoint.mode is explicit. ttl is 30m. 3 characters. has_more is true. first_id is the string first_id. 8 characters. last_id is the string last_id. 7 characters. 8 is not 7. The item id id is not first_id and is not last_id. object is list. The second example asks for input items on resp_abc123 and returns one item. The id is msg_abc123. 10 characters. 10 is not 2. type is message. 7 characters. role is user. The content type is input_text. The text is Tell me a three sentence bedtime story about a unicorn. 55 characters. 55 is not 4. The example prints no status. Do not copy in_progress onto it because the schema example had a status. first_id is msg_abc123. last_id is msg_abc123. The same 10 characters, and has_more is false. false is not true. One item whose first id equals its last id is not a list that has more, and a placeholder id of id is not msg_abc123. The role enum the reference names is user, system, and developer. system is 6 characters. developer is 9. 6 is not 4, and 9 is not 4. The sample role is user. Do not paste developer onto it because the enum lists the word (OpenAI, List input items).

The file example is a request with no input item id, and the response it prints is not an input. The request model is gpt-6-astra. 11 characters. The input is an array with role user and two content parts. The text is what is in this file? 21 characters, counting the question mark. type is input_text. 10 characters. The second part is type input_file. Also 10 characters, and not the same string. detail is auto. The file url is https://www.berkshirehathaway.com/letters/2024ltr.pdf. 53 characters. The response id is resp_686eef60237881a2bd1180bb8b13de430e34c516d176ff86. Also 53 characters, the same length as the file url, and not the same string. object is response. created_at is 1752100704. status is completed. 9 characters. completed_at is 1752100705. The gap is 1 second. The page does not print the gap. The output item id is msg_686eef60d3e081a29283bdcbc4322fd90e34c516d176ff86. 52 characters. 53 minus 52 is 1, a character, not the 1 second. type on that output is message. role is assistant. 9 characters, the same length as completed, and not the same string. assistant is not user. The content type is output_text, not input_text. The request never printed an input item id, so the message id in the output is not the input. input_tokens are 8438. output_tokens are 398. total_tokens are 8836. 8438 plus 398 is 8836. cached_tokens are 0. reasoning_tokens are 0. temperature is 1.0. top_p is 1.0. top_logprobs is 0. The schema prints a maxLength of 10485760 on input text. 10 times 1024 times 1024 is 10485760. 8438 is not 10485760. A token count on the response is not the character ceiling, and a reply about a shareholder letter is not the input text of 21 characters (OpenAI, Create a model response).

The image request on the same page is a different text, and the file-search request is a string. The image text is what is in this image? 22 characters. 22 is not 21. type is input_text beside type input_image. input_image is 11 characters. 11 is not 10. The response id is resp_67ccd3a9da748190baa7f1570fe91ac604becb25c45c1d41. 53 characters, the same length as the file response id, and not the same string. created_at is 1741476777. completed_at is 1741476778. The gap is again 1 second. 1752100704 minus 1741476777 is 10623927 seconds: 122 days, 23 hours, 5 minutes, and 27 seconds. The page does not print that gap. Two responses with a one-second internal gap are not one response a season apart. The file-search request sets input to the string What are the attributes of an ancient brown dragon? 51 characters. 51 is not 21, and 51 is not 55. It is a string, not an item with an id. tools names type file_search, vector store vs_1234567890, 13 characters, and max_num_results 20. 20 is not 51. The response id is resp_67ccf4c55fc48190b71bd0463ad3306d09504fb6872380d7. 53 characters. The file-search call id is fs_67ccf4c63cd08190887ef6464ba5681609504fb6872380d7. 51 characters. The message id is msg_67ccf4c93e5c81909d595b369351a9d309504fb6872380d7. 52 characters. All three end in 09504fb6872380d7. 16 characters. A shared suffix is not a shared id. 51 is not 52, and 52 is not 53. The call type is file_search_call. That is an output item. The input on that request was a string with no item id. Do not paste msg_abc123 onto it because both pages are about responses.

The averages people quote when they want an input risk did not open this log. Yupei Liu, Yuqi Jia, Runpeng Geng, Jinyuan Jia, and Neil Zhenqiang Gong, at Penn State and Duke, evaluate 5 prompt injection attacks and 10 defenses with 10 LLMs and 7 tasks. 7 times 7 is 49 target and injected task combinations. 5 is not 10, and 10 attacks is not what the abstract prints. The abstract prints 5 attacks and 10 defenses. They take 100 examples per task as target data and 100 as injected data, with no overlap, which is 10,000 pairs, and they then sample 100 pairs to compute the attack success value. 100 is not 10,000. The sample they score is 100 of 10000 pairs. 49 times 100 is 4,900, which is not 10,000 either, because the 10,000 is pairs inside one task combination, not one pair per combination. The page does not print 4,900. Table 4, GPT-4, averaged over the 49 combinations, prints 0.62, 0.66, 0.65, 0.70, and 0.75 for the five attacks, and the last of those is the combined attack. A later sentence says the combined attack's success value and matching rate, averaged over the 10 LLMs and the 49 combinations, are 0.62 and 0.78. 0.62 in that sentence is not the 0.75 in Table 4, and it is the same written figure as Table 4's first attack, which is not the combined attack. 0.78 is not 0.62. Table 6 prints a target-task success of 0.64, 0.59, 0.63, 0.64, 0.64, 0.59, and 0.62. The unweighted mean of those seven printed figures is 0.621, which is not a figure the page prints. The Pearson correlation they give between average success and model size is 0.63, and between matching rate and model size is 0.64. 0.63 is not 0.64. 0.63 is also the hate-detection cell in Table 6, which is not a correlation. Closed models are queried at temperature 0.1. The file sample above prints temperature 1.0. 1.0 is not 0.1. The paper's default model in that discussion is GPT-4. gpt-6-astra is not GPT-4. None of these is msg_abc123, and none of them is the 55-character bedtime story (Liu, Jia, Geng, Jia, and Gong, Formalizing and Benchmarking Prompt Injection Attacks and Defenses).

Do the three proofs on one real response, in the log that stored the call. First, the binding. Copy the input item id, the type, and the text. If the request was a string and there is no item id, write that the log stored no item id, and copy the string. If the type is input_file, write the file url or the file id, and write that the url is not the response id even when the character counts match. If status is absent, write absent. Do not borrow in_progress from a schema example. Do not infer the bedtime story because a sample prints it. Second, the other item. A different id, even with the same role user, is not this one. The two list examples are that shape, and they are still samples. An attack average is not that second item. Third, the other log. A list that belongs to another response does not contain this id. If the only list is one shared assistant, write that. A tenant you cannot name is the finding.

Three moves feel like the proof and are a second problem. Pasting the reply back into a new call and asking which input it was, which creates a new response and a new input. Editing the text in the source tree and calling the file the input that already ran. Quoting 0.75 or 0.62 because they look precise, while the input you were asked about is one item, and the paper that printed them uses the figure 0.62 for two different averages. Asking the model which input was sent. The model will pick a plausible sentence. It will not open the item. A search call you have not kept is which search did that response call?. Opening that call does not name this input id. The system text on a chat request is which prompt did that request send?. A system string is not an item id.

The path is ordinary when an operator can open one customer's response without opening the next customer's response as the way to prove it. Veritas AI is a fraud detection API for multi-tenant SaaS: behavior and anomaly models, and an enterprise dashboard, distinct from the next customer's log. It is not an input-item ledger. Nothing in that sentence is a headcount, a duration, or a result figure. If the only record is the customer's complaint, you do not have this proof. You have a ticket.

There is a version of this call 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 an item id does not close a leak. The wrong value was already written back to a system of record. An input log does not undo a write. Or no metric was ever named, so there is nothing for a later eval to move. The criteria for choosing a firm sit on the page written for a top SaaS development company. That page is criteria. It does not rank anyone, including us. 0.75 is not your input, and the bedtime story is not your input.

When the feature is already live and the log does not store the input, the missing object is specific. A record, in an account you administer, of the item id, the type, and the text, or a written line that the request was a string, plus a different item that is not that one, plus another response's list that does not hold it. An AI pilot failed to reach production engagement is scoped after an audit, not before it. The audit is the first one to two weeks. The engagement runs eight to twelve weeks. The build is unpaid if the agreed metric does not move. Rollout, when it happens, sits behind a human-review gate. If you do not yet know whether the pilot can be saved, a two-week AI readiness assessment hands you the findings either way. AI evaluation and observability is how you learn, afterwards, whether the cases you saved still fail when the input changes. The paper's averages will not be that score. 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 anyone which input the response sent, do the three proofs and write the id. Name the id, the type, and the text, or write that the request stored a string and no item id. Show a different item that is not that one, even if the role is user. Show another response's list that does not contain it. Do not paste output_text onto an input_text because both samples are messages, and do not paste 0.62 from the all-model sentence onto Table 4's combined attack. The binding is the input you can defend.