What the documents look like
Convenience-store fresh food is ordered frequently by store staff against short shelf lives. Store managers work from POS sales, the headquarters ordering system, store inventory and cold-chain temperature records to handle ordering and damage write-offs each day or each shift. This differs from a hypermarket fresh-food concession counter settled by revenue share. Order fulfillment and after-sales answering for this category center on four document types: the fresh-food order form, the damage report, the near-expiry handling sheet and the temperature record. Expired sales, stock-out losses and food-safety complaints are the direct consequences of getting this wrong, so a multi-turn prompt must state the material scope, the answering task and how to behave when evidence is missing, without letting the model invent business facts. History records need checking against the context actually carried in, distinguishing a new session from a continued session. Question optimization is checked only in knowledge-base continuous dialogue for pronoun resolution and question expansion, and short questions are not assumed to all qualify. Model and length settings are checked against the model's supported context and reply settings, testing long documents combined with follow-up questions. Reply format is chosen by what downstream accepts; when json_schema is selected, the format requirement is filled in and the returned structure verified.
Constraints at the multi-turn prompts step
After-sales answering for fresh-food orders often involves near-expiry handling, damage registration and temperature-exception follow-ups. A multi-turn dialogue usually needs only a small number of history turns retained, the temperature for customer-service and knowledge-base answering is suggested to be on the low side, and the reply cap is lowered as needed. Retrieval returns knowledge-base content and does not perform authorization; a similarity score is not a category, entity or permission filter, and cannot be used to exclude unrelated businesses or unauthorized records. Libraries using different vector models cannot be associated together; identifiers are checked first and non-matching ones excluded, recording only what is actually observed. Empty-result handling can be checked against an explicit paper input for downstream design, without claiming that a fictitious rule search must return an empty array. An interactive node waits for a user choice or form input; a form collects values, and business verification, approval, database writes and external synchronization each need their own downstream implementation. The following is a hypothetical drill sample for checking prompts and draft sentences, pointing to no real store or person.
Hypothetical drill sample: the source material reads "Fresh-food order form A-001, store M, order date 2025-03-10, rice balls 120 units, arrival date 2025-03-11; damage report B-002, store M, report date 2025-03-12, rice balls 30 units, reason near-expiry; near-expiry handling sheet C-003, store M, registration date 2025-03-12, rice balls 20 units, handling method discount; temperature record D-004, store M, record date 2025-03-11, refrigerated cabinet 4 degrees Celsius." The draft sentence to be judged is "Store M completed approval of the 30-unit rice-ball damage write-off on 2025-03-12 and synchronized it to the headquarters system." That sentence concerns approval and synchronization, while the material shows only damage registration, with no approval signature or system-synchronization evidence.
Configuration and implementation checks
The table below lists, around this page's sample, official documented settings or implementation checks relevant to this feature and primary task. Where a value is needed, it is stated as this hypothetical trial's starting point and the value it is compared against.
| Configuration item | Hypothetical trial starting point | Check method |
|---|---|---|
| Memory turns | Retain a small number of history turns, compared with the default | Check the context actually carried in, distinguishing new from continued sessions |
| Temperature | Low side, compared with the customer-service suggestion | Check answer stability; do not use it to exclude unauthorized records |
| Reply cap | Lowered as needed, compared with truncation symptoms | Check whether long documents combined with follow-ups are cut off early |
| Reply format | Default, compared with a json_schema requirement | When json_schema is selected, fill in the format requirement and verify the returned structure |
| Multimodal recognition | Chosen by model capability, compared with what the dialog shows | Image recognition requires an image input and model capability |
| Interactive node | Configured as needed, compared with the waiting state | A form collects values; approval and synchronization need downstream implementation |
Three common errors
- Treating retrieval similarity as a permission filter. Input is a retrieval request for "store M rice-ball damage records"; the similarity score is compared with the authorization scope, and excluding unauthorized records on that basis is an error. Retrieval returns content; authorization is checked separately.
- Treating pending review as completed approval. Input is the draft sentence "completed damage approval and synchronized"; the material's damage report B-002 shows only damage registration, and judging it as approved is an error. Pending review does not demonstrate that approval is complete.
- Treating form collection as business execution. Input is the damage quantity returned by the interactive-node form; whether a downstream write or synchronization implementation exists is compared, and judging it as synchronized is an error. A form collects values; business verification and synchronization need downstream implementation.
How to verify the setup
- Input the sample source material and the draft sentence; compare the draft sentence "completed damage approval and synchronized" with the material's damage report B-002 showing only damage registration, and observe whether the evidence gap for approval and synchronization is preserved. If the draft sentence is judged unsupported, the prompt's handling of missing evidence meets expectations.
- Input an empty-result paper array; compare whether the downstream node follows its empty-result handling design, and observe whether a fictitious rule search return appears. If the downstream processes the empty array as such, empty-result handling meets expectations.
- Input library identifiers across different vector models; compare whether the identifiers match, and observe whether non-matching ones are excluded and only actual observations recorded. If no cross-model retrieval comparison is performed to infer missed recall, identifier checking meets expectations.