What the documents look like
Brand-owned stores are staffed and accounted for directly by the brand, without franchise contracts, so the material behind copy drafting comes from internal systems: the POS system, member CRM, ERP inventory, and store inspections. The documents handled day to day include daily sales reports, shift schedules, display photos, and inventory transfer orders. These records set the ceiling for what product and marketing copy can say. Stock counts, on-sale status, and campaign scope come from internal records, and copy can only be drafted within the range those records support.
This page covers only one step: multi-turn dialogue and prompts for product and marketing copy drafting. It does not describe batch synchronization, full synchronization, real-time updates, or transaction-triggered mechanisms. Where update frequency matters, the source material only states daily and weekly updates.
Constraints at the multi-turn prompts step
The value of multi-turn dialogue is the ability to keep asking about the same set of materials. A prompt needs to state the material scope, the answering task, and how to handle missing evidence, so the model does not fill in business facts on its own. History determines which context is carried in: a new conversation and a continued conversation receive different inputs, and the check should distinguish them. When a knowledge base needs continuous dialogue, the check covers reference resolution and question expansion, and does not assume every short question qualifies. For model and length, check the context the model supports and the reply settings, and test the combination of a long document with follow-up questions. Reply format follows what comes next; when json_schema is selected, fill in the corresponding format requirement and verify the returned structure.
Hypothetical exercise sample: the source material reads, "This store campaign is limited to Store A, runs from March 1, 2025 to March 15, 2025, covers spring knitwear, and has 40 units in stock." The draft sentence reads, "Spring knitwear campaign is running across all stores citywide, welcome to shop." Points to judge: the draft sentence states the campaign scope as all stores citywide, while the material states Store A only; the draft sentence omits the campaign period and the stock range. The conclusion is unsupported, and the draft must be rewritten back within the material scope.
Configuration and implementation checks
The table below lists settings already described in the official documents, or implementation checks tied to this feature and this primary task. The values are starting points for this hypothetical exercise. They state the reason for the choice and the comparison object, and are not official default values.
| Configuration item | Hypothetical starting point | Check method and basis |
|---|---|---|
| AI model | Choose a model that supports the needed context length | Compare the maximum context shown in the model information against the total length of the sample material plus follow-up questions |
| Memory rounds | Start with a smaller number of rounds | Compare against the history rounds the sample needs, and observe whether campaign scope and period are carried in |
| Reply limit | Start with a length that fits the full copy | Compare against the complete draft sentence output, and observe whether it is cut off early |
| Temperature | Use a lower value | Compare against the stability requirement for copy; rule-defined scenarios use lower values |
| Reply format | Keep the default | When a downstream flow needs to read a fixed structure, switch to json_schema and fill in the format requirement |
| Multimodal recognition | Decide whether to enable image recognition based on model capability | When the sample includes display photos, check whether image recognition is available |
Three common errors
- Expanding the campaign scope in the material from Store A to all stores citywide. The error is that the draft sentence exceeds the subject scope the material supports. Check: input the source material and the draft sentence, compare the campaign scope field, and a store scope the material does not state means the draft exceeds it.
- Omitting the campaign period and stock count from the draft sentence and still judging it complete. The error is treating missing evidence as supported. Check: input the source material and the draft sentence, compare the campaign period and stock fields, and the absence of those items means a gap that is recorded as pending.
- Using a similarity score to infer that the copy is authorized for release. The error is treating retrieval relevance as a permission or entity filter. Check: input the
scorereturned by retrieval and the draft sentence, compare against whether an authorization record exists, and with no authorization record the result is recorded as pending verification.
How to verify the setup
- Verification one: input the source material "This store campaign is limited to Store A, runs from March 1, 2025 to March 15, 2025, covers spring knitwear, and has 40 units in stock" and the draft sentence "Spring knitwear campaign is running across all stores citywide, welcome to shop." Compare the campaign scope field. The material states Store A and the draft states all stores citywide; the two take mutually exclusive values on the same field, and when this condition holds the result is judged as exceeding the material scope.
- Verification two: input the same source material and the draft sentence, and compare the campaign period and stock fields. The material states March 1 to March 15 and 40 units, while the draft sentence contains neither; when this condition holds the result is judged as unsupported, and the evidence gap is retained.
- Verification three: input a handoff request, "Please submit the draft sentence above for release." Compare this request against the authorization record. With no authorization record, the request is in a pending review state; a pending request does not constitute approval, and when this condition holds the result is judged as not executable and recorded as pending verification.
Retrieval checks record only observations and failure branches. An empty result can be checked with an explicit paper input of [] against the downstream handling design, without claiming that a fictional rule retrieval necessarily returns []. Libraries using different vector models cannot be associated together; only check the identifier and exclude non-matching ones, without running a cross-model retrieval to compare missed recalls. The optional paths listed in the overall document flow do not mean every request executes all of them; image retrieval requires a corresponding image input and model capability, and reranking requires a model and must be enabled by configuration.