What the documents look like
The data for this category comes from the Ministry of Commerce reporting system, POS plastic bag sales, the procurement system, and store ledgers. It includes procurement ledgers, sales vouchers, semiannual reports, and self-discipline commitments. Daily updates and semiannual reporting coexist, so prompts must state which materials are being used and what time range applies, avoiding any mixing of the two.
Constraints at the multi-turn prompts step
Assume a drill sample: an excerpt from a supermarket store’s “Plastic Bag Procurement Ledger” reads, “From March 1 to March 31, 2025, this store purchased 12,000 medium shopping bags and 6,000 large shopping bags; the supplier is Factory A, and the delivery note number is A-0331.” A draft “Semiannual Report” reads, “In the first half of 2025, this store’s plastic bag usage decreased compared with the same period last year.” The sentence to be judged is, “According to the ledger, this store purchased 18,000 plastic bags in March, and it is confirmed that usage declined in the first half of the year.” The objects to check are the original text of these two materials and the drafted sentence.
Prompts must specify which categories are being relied on among the procurement ledger, sales vouchers, semiannual report, and self-discipline commitment, and must state that no answer can be given when evidence is missing. For continued sessions, the session is retrieved from the database and the last message is treated as the user question; a new session does not use context and relies entirely on the passed-in messages. Question optimization checks referent resolution during continuous knowledge-base dialogue, and whether short questions apply must be checked in practice. The model’s maximum context and reply limit must be checked, and long documents combined with follow-up questions must be tested. The reply format is chosen according to downstream handling; when json_schema is selected, the corresponding format requirements must be filled in and the returned structure verified.
In the sample, the drafted sentence directly equates ledger procurement volume with usage volume and connects March single-month data to a first-half conclusion. Both points exceed the valid scope of the original materials. The ledger records procurement quantity and supplier, while the semiannual report draft gives only a trend judgment and no verifiable numerical value. The existence of materials does not mean submission has been completed, a draft does not mean it has been submitted, and a procurement record does not mean environmental review has passed.
Configuration and implementation checks
The table below centers on the above sample and lists officially described settings or implementation check items related to this function and the main task of order fulfillment and after-sales question answering. The values in the table are only starting points for this hypothetical test, used for comparison with the original materials, and are not official default values.
| Configuration item | Starting point for this hypothetical test | Verification method and reason |
|---|---|---|
| AI model | Choose a model that supports the required context length | Check maximum context and tool-calling capability, and confirm it can accommodate the combined length of the ledger and report draft |
| Memory turns | Keep a small number of historical turns | Customer service and knowledge-base Q&A scenarios usually need only a small number of historical turns; check the actual historical messages brought into a continued session |
| Reply limit | Set according to answer completeness | If set too low, truncation may occur; check whether the answer fully covers the material scope |
| Temperature | Low | Answers are more stable in scenarios with clear rules; check whether answers are consistent under the same input |
| Reply format | Default | Change to json_schema only when a downstream process needs to read a fixed structure, and fill in the corresponding format requirements |
| Multimodal recognition | Determined by model capability | This option is not displayed when the model does not support it; check whether the pop-up provides it |
The configuration pop-up displays available items according to the current model capability. When the model does not support multimodal recognition or thinking configuration, the corresponding options are not displayed. Thinking intensity aligns with reasoning_effort, and the thinking budget controls the thinking process; these are different concepts from the cited token limit. Retrieval and similarity settings do not assume responsibility for permission or entity isolation, and retrieval results do not constitute authorization.
Three common errors
- Writing the procurement ledger quantity directly as usage volume. The ledger contains only procurement quantity, supplier, and delivery note number, not usage volume; the drafted sentence should be compared with the ledger fields, and if unsupported, this should be recorded.
- Treating the semiannual report draft as an already submitted report. The draft gives only a trend judgment, with no submission status, date, or receipt; it should be compared to see whether a submission action and recipient are stated, and any absence should be recorded as pending verification.
- Treating historical messages in a continued session as the basis for the current question. In a continued session, the last message is the user question and the rest are ignored; the actual context brought in should be compared with the current question to confirm that the answer is based on the current input.
How to verify the setup
- Input the original text of the two materials in the above hypothetical sample and the drafted sentence. Compare “18,000 bags” in the drafted sentence with the sum of 12,000 and 6,000 in the ledger original text; if the values match, that part can be verified. Compare “usage declined in the first half of the year” with the original text of the semiannual report draft; because the draft gives only a trend judgment and no usage figure, record that part as unsupported.
- Input a continued-session request with chatId as a non-empty string and the last message in messages as “How many bags were purchased in March?” Compare the context on which the answer is based with the current question to confirm that the answer relies on the current input. Then input a request without chatId and compare whether it relies entirely on the passed-in messages.
- Input a question involving submission status and compare the answer with whether the materials state a submission action, recipient, and receipt; when missing, record the evidence gap. If the configuration selects json_schema, check whether the returned structure meets the filled-in format requirements; if not, record the actual returned content.
The above is a paper-based verification design and has not actually been executed. Retrieval checks record only observations and failure branches and do not guarantee a first-hit result or full recall. Empty-result handling can use an explicit paper input [] to verify the downstream design. Libraries across different vector models cannot be jointly associated; first check identifiers and exclude those that do not conform.