What the documents look like
Brand-owned stores are staffed and accounted for directly by the brand, so order fulfilment and after-sales question handling rely on records the store itself produces: the POS system, member CRM, ERP inventory, and store inspections. The documents in scope here are daily sales reports, shift schedules, display photos, and inventory transfer orders. These materials update daily and weekly; batch synchronisation and real-time triggers sit outside this page.
Three properties of these materials shape the multi-turn prompt task. A daily sales report records only the state of one day, so a question about an earlier period needs the corresponding report for that period. An inventory transfer order records both an application and an execution status is pending review is not approval, and approval is not dispatch, so a multi-turn follow-up has to check each stage separately. Display photos and shift schedules do not carry order or after-sales amounts, and treating them as fulfilment evidence introduces unrelated content.
The public brief describes this category at a general level. The specific paper sample used below is a hypothetical construction for this page, not a record of any real store, order, or test.
Constraints at the multi-turn prompts step
Memory turns control how many earlier rounds the AI may reference. A larger value makes earlier dialogue easier to reach and also carries more content into the request; customer-service and knowledge-base applications typically keep a small number of history turns. Stop sequences, response format, temperature, and Top_p are configurable fields, and ordinary customer-service question answering keeps them at default.
History handling depends on chatId. When chatId is empty, the FastGPT context feature is not used and context is built entirely from the supplied messages. When chatId is a non-empty string, the session is read from the database automatically, the last item in the messages array is used as the user question, and the remaining messages are ignored.
Question optimisation for continuous knowledge-base dialogue requires checking reference resolution and question expansion; short questions do not all behave the same way. Maximum context is the length of content a model can reference in a single request, so the model's supported context and the reply settings need to be checked against the material. Response format follows the downstream consumer; selecting json_schema requires filling in the corresponding format requirement and verifying the returned structure.
Retrieval and authorisation are separate. A similarity score is not a category, entity, or permission filter, and retrieval does not constitute authorisation. Forms collect values; business verification, approval, database writes, and external synchronisation each need their own downstream implementation.
Configuration and implementation checks
The table lists documented settings or implementation checks relevant to this page's feature and primary task, tied to the hypothetical sample below.
| Configuration item | Value or check on this page | Basis and comparison method |
|---|---|---|
| AI model | Choose a model whose maximum context covers the sample material | A larger maximum context suits longer dialogue; compare against the length of the sample text |
| Memory turns | Hypothetical starting value of 2 for this paper exercise | Customer-service cases usually keep few history turns; compare 0, 2, and 4 turns to see whether the follow-up still reaches the earlier statement |
| Reply cap | Keep default, no extra restriction | A low setting can truncate the reply early |
| Temperature | Low | Customer service and knowledge-base question answering are advised to use a low value |
| Response format | Keep default | Ordinary customer-service question answering keeps default; switch to json_schema only when a downstream step reads a fixed structure |
| Stop sequences | Not set | Ordinary chat generally does not need them |
The reason for starting memory turns at 2 is that the sample contains one prior round plus one follow-up. The comparison method is to rerun the same follow-up at 0 and at 4 turns and observe whether the reply still references the status word in the sample text.
Three common errors
- Treating a retrieval score as a permission filter. In the hypothetical sample, the returned score expresses similarity only; it does not show that the record belongs to this store or that the viewer is authorised. The faulty action is deciding whether to display transfer-order content based on how high the score is.
- Treating pending review as approved. The transfer order in the sample carries the status pending review; a draft sentence reading "the transfer has been approved" converts an unfinished state into a completed one.
- Treating an unstated field as a denial. The sample material does not state an after-sales handling deadline; a draft sentence reading "this order does not support after-sales service" draws a negative conclusion from missing evidence.
How to verify the setup
Hypothetical paper sample, not actually run. Material text: "2024-06-11 daily sales report: Store A closed 42 orders that day, with 1 return. Inventory transfer order number DB-0611-07: requesting store A, item M, quantity 6, status pending review." Draft sentence: "Store A's transfer on 11 June was approved, and the after-sales deadline is 7 days."
- Input the material text above together with the draft sentence, and compare "was approved" in the draft against "status pending review" in the source. The condition is met only when the draft is changed to "status pending review" or is explicitly marked as awaiting verification; a draft reading "was approved" does not meet it.
- Input the same material text and draft sentence, and compare "the after-sales deadline is 7 days" against whether any deadline field appears in the source. The field is not stated, so the sentence is unsupported; the evidence gap stays open and the sentence is neither a denial nor a conflict.
- Input two requests, one with chatId empty and one with chatId as a non-empty string, and compare the context the model actually carries. With an empty chatId the context is built entirely from messages; with a non-empty chatId the last message content becomes the user question and the rest are ignored. Observe whether the follow-up still references the status word in the sample text under each condition, and use that observation to judge whether the memory-turn setting is suitable.
This page covers only brand-owned store operations, order fulfilment and after-sales question handling, and multi-turn prompts. Whether materials, operations, or requests are authorised cannot be inferred from retrieval results or from a form.