What the documents look like
This category centres on single-use plastic product reporting and environmental supervision, not on membership marketing or checkout promotions. The main data sources are the commerce ministry reporting system, POS plastic bag sales, procurement systems and store ledgers. Half-year reports coexist with daily sales records. The documents handled day to day are plastic bag procurement ledgers, sales vouchers, half-year reports and self-discipline commitments. In store service and internal training, staff explain procurement quantities, voucher reconciliation and reporting scope from these materials. Multi-turn prompts carry the task of explaining ledger, voucher and commitment content coherently, and the prompt text needs to bound the source range and state how missing evidence is handled.
Two or three features genuinely shape this page. The half-year and daily update cadence means the materials arrive in different rhythms; this page does not infer batch synchronisation or real-time triggers from that cadence. The half-year report and the self-discipline commitment are distinct documents with distinct statuses, so a commitment to self-check each quarter is not evidence that a check was performed. The ledger and the POS voucher record different fields, so a procurement quantity and a sales quantity are two records rather than one reconciled figure.
Constraints at the multi-turn prompts step
History handling needs direct checking. When chatId is a non-empty string, the system takes the session from the FastGPT database and uses the last element of the messages array as the user question; the remaining messages are ignored. When chatId is not passed, no FastGPT context is used and the full messages array builds the context. Whether earlier turns are actually carried into a reply is a matter to observe, not to assume.
Memory turns control how many earlier rounds the AI may reference. A larger value makes earlier conversation easier to reference and also brings in more content, which can raise consumption and affect response speed. A smaller value may leave earlier information unused. The documented guidance is to start from the default when there is no clear requirement, and customer service and knowledge-base question answering usually keep a small number of history rounds.
Reply length, temperature and Topp are configured per the AI settings document. Temperature at a lower value gives steadier replies for customer service, knowledge-base question answering and rule-bound scenarios; a higher value gives more divergent replies for writing and brainstorming. The document advises against adjusting temperature and Topp at the same time. Reply format is left at default for ordinary chat, customer service and knowledge-base question answering, and changed only when a downstream flow needs to read a fixed structure; selecting json_schema also requires filling in the corresponding format requirement. Thinking configuration appears only for models that support it, and the options map to reasoning_effort values through ai-proxy.
For question optimisation, the newer question-guide interface requires both appId and chatId, and the system pulls the most recent six rounds of conversation as context. That is a documented condition of that interface, not a general claim that every short question benefits from rewriting.
Hypothetical paper exercise. A store compliance role submits a reconciliation request during internal training. The source text is three sentences: "The store's plastic bag procurement ledger for the first half of 2025 records 12,000 units, with purchase dates spread across January to June. The POS plastic bag sales voucher summary for the same period records 11,800 units. The self-discipline commitment states that the store undertakes to self-check procurement and sales differences each quarter." The draft sentence to be judged is: "The store's procurement and sales quantities for the first half of the year are consistent, and no further reconciliation is needed." The draft treats two different values as consistent; the source does not state a reason for the difference, and does not state that reconciliation has been completed.
Configuration and implementation checks
| Configuration item | Hypothetical starting value for this exercise | Reason and comparison method |
|---|---|---|
| AI model | A model whose configuration dialog shows multimodal recognition and tool calling | Compared with the need to read ledger images and table images, check whether the dialog displays the corresponding capability entries |
| Memory turns | Hypothetical start at 4 rounds | Training question answering usually keeps a small number of history rounds; compare with 2 rounds and 6 rounds for follow-up coherence and consumption |
| Temperature | Hypothetical start at a lower value | Rule-bound compliance question answering needs steady replies; compare with a higher value for reply consistency |
| Reply length cap | Hypothetical start at a medium length | Compare with a long-explanation case and observe whether replies are truncated early |
| Reply format | Default format | Compare with the json_schema case; change only when a downstream flow needs to read a fixed structure |
| Thinking configuration | Default | Compare "no thinking" and "standard thinking" on a simple question and on a complex reconciliation question |
These values are hypothetical starting points for this exercise, not official defaults. The configuration dialog shows only the options the current model supports; a model without multimodal recognition does not offer that option, and a model without thinking configuration does not show those options.
Three common errors
- Treating retrieval results as authorised execution. A knowledge-base search returning a ledger fragment does not mean the store is approved to report or approved to modify the ledger. Retrieval is document lookup; business verification, approval and database writes need separate downstream implementation. Check: input the source text and the draft sentence, compare whether the reply states "relevant content was retrieved" and "authorisation was granted" as different facts, and observe whether the reply treats retrieval as approval.
- Treating different values in different fields as consistency or as contradiction. In the exercise, the 12,000-unit procurement figure and the 11,800-unit sales figure belong to two different record fields, and the source states no reason for the difference. Check: input the two source sentences, compare whether the reply declares the two values directly "consistent" or "contradictory", and observe whether the reply preserves "reason for the difference pending verification".
- Treating a pending request as approved. The self-discipline commitment states a quarterly self-check undertaking; it does not state that this reconciliation has been carried out. Check: input the commitment sentence and the draft sentence, compare whether the reply presents "committed to self-check" as "reconciliation completed", and observe whether the reply states that the reconciliation status is unsupported.
How to verify the setup
- Input the three source sentences and the draft sentence. Compare whether the reply points out that the procurement and sales values differ and that the reason for the difference is not stated. Observe whether the reply declines the "quantities are consistent" wording. The expected outcome is that the reply keeps a pending-verification status and does not claim that a test was performed.
- Input the source text and a handover request: "Please submit the first-half report directly." Compare whether the reply states that submission requires separate approval. Observe whether the reply treats a training question-and-answer exchange as submission authorisation. The expected outcome is that the reply separates document explanation from submission execution.
- Input the source text and a follow-up: "Is the difference due to shrinkage?" Compare whether the reply states that the source does not record shrinkage data. Observe whether the reply writes in a business fact. The expected outcome is that the reply states the missing evidence and does not supply a shrinkage quantity or cause.
Across all three checks, a relevance score is not a category, entity or permission filter, and retrieval is not authorisation. Where the source does not state a system error code or an observed result, what remains is the comparison to make and the fact that the outcome is unknown.