What the documents look like
Price and display auditing for chain stores happens during ordinary trading periods, and the central concern is sustained execution of standards. The working materials are supervisory visit photographs, price-tag lists, display standards and rectification notices, drawn from supervision systems, POS price lists, store cameras and head-office promotion calendars. Updates arrive weekly or monthly, and promotion windows carry explicit start and end dates.
Two features of this category shape the multi-turn prompt step. First, the same store can face different price-tag requirements in different promotion windows, so a prompt must lead the model to confirm which window and which store the current question concerns. Second, supervisory photographs and price-tag lists serve as check materials, and the model can only answer from the text content carried into the conversation; it cannot infer details the photograph description does not state. Historical records determine how many earlier turns are carried into a follow-up question, and a new conversation and a continued conversation have different input scopes. Where a later step needs to read a fixed structure, the reply format can be set to json_schema with the corresponding format requirements filled in and the returned structure checked.
Constraints at the multi-turn prompts step
The prompt must state the material scope, the answering task and how to behave when evidence is missing, so the model does not supply business facts on its own. For a follow-up question, the actual context carried in must be checked, and the difference between a new conversation and a continued one must be kept clear. Question optimisation applies to continuous knowledge-base dialogue, where pronoun resolution and question expansion are checked; short questions do not all qualify.
Hypothetical drill sample, marked as such and not drawn from any real record: on 10 March 2025 a supervisor in a chain brand's operations department opens a conversation with chatId store-0310-a, carrying the following material text. "Store A, within the promotion window from 1 March 2025 to 31 March 2025, has a price-tag list showing item 3301 priced at 12.9 yuan. The remarks field of the supervisory photograph states that this item is displayed on the second shelf of the end display." The draft sentence to be assessed is: "Store A item 3301 executes a 12.9 yuan tag in the March window, and the display position meets the second-shelf end-display requirement." The check compares the draft sentence with the material text on subject, object, period and field.
Configuration and implementation checks
The table below lists settings the official documentation describes, or implementation checks, tied to this sample and this task. The values are starting points for this hypothetical drill, used for comparison against the carried material, and are not official defaults.
| Configuration item | Hypothetical starting point for this drill | Reason and comparison method |
|---|---|---|
| AI model | Choose a model that supports multimodal recognition | Supervisory photographs may arrive as image input; check the model capability before enabling the matching recognition type |
| Memory turns | Set to 3 turns | The store manager may follow up on the window and the photograph; compare whether the carried turns cover the context the follow-up needs |
| Reply limit | Enabled and set to a medium length | The sample answer needs item, window, price and display position; compare whether truncation drops a field |
| Temperature | Low | Price and display checks require stability; compare whether repeated answers to the same input stay consistent |
| Reply format | Default | The sample needs a text answer only, with no downstream fixed-structure reading |
| Multimodal recognition | Image recognition enabled | When a supervisory photograph arrives as image input, check whether the model supports it and converts it to recognisable input |
Three common errors
- Treating the window range in the carried material as permanently valid. The sample text states 1 March to 31 March 2025. A draft sentence reading "executes 12.9 yuan long term" has a period field that does not match the source, and the statement goes beyond what the material supports.
- Treating the display position in the photograph remarks as a verified conclusion. The remarks field states "second shelf of the end display". A draft sentence reading "meets the second-shelf end-display requirement" has no support, because the source text does not state whether that position satisfies the display standard.
- Ignoring the history tied to a chatId when continuing a conversation. If the store manager follows up under the same chatId about a different item, the carried context must be checked for that item's price-tag record in the March window; where it is absent, record the gap.
How to verify the setup
- Enter the sample material text and the draft sentence, then compare subject, object, period and field in the draft against the source. Where the draft reads "Store A item 3301 executes a 12.9 yuan tag in the March window", the period and price match the source and that part meets the condition. Where it reads "long term", the period field does not match and the statement is recorded as beyond what the material supports.
- Enter the photograph remark "displayed on the second shelf of the end display" and compare it with the draft sentence "the display position meets the second-shelf end-display requirement". The source does not state the specific requirements of the display standard, so this judgement is recorded as unsupported, and the display standard text must be supplied before the check can continue.
- Under the same chatId, send a follow-up carrying another item's price-tag record, then compare whether the model's answer cites that item's record for the March window. Where the answer cites data from another window that was not carried in, record it as a context-scope anomaly. Where the answer relies only on the carried record, the condition is met. For empty-result downstream handling, a clearly stated paper input of [] can be used to check the downstream design; no claim is made that a fictional rule search returns [].
An unprovided document field remains unknown. A relevance score is not a category, entity or permission filter, and retrieval does not establish authorisation. Forms collect values; business verification, approval, database writes and external synchronisation require their stated downstream implementation.