What the documents look like
Douyin storefront operations do not depend on the rhythm of a single live session. Daily attention goes to product cards and the store experience score. This task covers order fulfillment and after-sales question answering, and multi-turn dialogue draws on four document types: product qualification pages, experience score detail, shipping violation appeals, and store announcements. A shared characteristic is that the same order or the same product can appear with different statuses across documents, and those statuses update daily or per order. When a multi-turn conversation carries a status from an earlier turn into the current turn, it can treat a changed qualification or experience score as current fact. Prompts therefore need to state the material scope and the answering task, and preserve gaps when evidence is missing instead of letting the model fill in business facts.
Constraints at the multi-turn prompts step
The history carried into a multi-turn conversation determines which materials the model can see. A new session does not automatically inherit materials from a previous session. A continuing session retrieves the conversation from the database by chatId and uses the last element of the messages array as the user question, ignoring the remaining messages. Prompts need to distinguish these two input modes. Question optimization, when used for continuous knowledge base dialogue, is checked for reference resolution and question expansion, and short questions do not always fit. Model context and reply limits affect long documents combined with follow-up questions. Reply format follows what downstream steps need; when json_schema is selected, the corresponding format requirements are filled in and the returned structure is checked.
Configuration and implementation checks
Hypothetical paper exercise: a Douyin store merchant submits a shipping violation appeal on March 10, 2026. The source text reads: "Store order 20260309001 was out of stock on March 9, 2026; an appeal was submitted per platform requirements; the current status is pending review." A separate experience score detail reads: "March 9, 2026 experience score 4.6." The draft sentence under review is: "The out-of-stock appeal for this order has been approved and the experience score has recovered." The appeal material states only pending review, so the draft treats a pending status as approved, which the material does not support.
| Configuration item | Hypothetical starting point for this exercise | Reason and comparison object |
|---|---|---|
| AI model | Choose a model that supports the needed context length | Compare against material length; no specific model named |
| Memory turns | Set to a small number of history turns | Compare against prior context needed for fulfillment Q&A; customer service typically keeps a small number |
| Reply limit | Adjust by answer completeness | Compare against truncation behavior; no specific value written |
| Temperature | Set low | Compare against the stability requirement for customer service and knowledge base Q&A |
| Reply format | Keep default | Compare against whether downstream needs a fixed structure |
| Multimodal recognition | Decide by model capability | Compare against whether the user uploads images, audio, or video |
Three common errors
- Writing a pending appeal as approved. The input is the source text "the current status is pending review"; the comparison object is the draft sentence "the appeal has been approved"; the observable result is that the draft states a conclusion the material does not contain.
- Mixing the experience score detail date with a recovery conclusion in the draft. The input is "March 9, 2026 experience score 4.6"; the comparison object is the draft sentence "the experience score has recovered"; the observable result is that the draft lacks a basis for recovery.
- Treating retrieval results as authorization. The input is an appeal record returned by retrieval; the comparison object is whether the draft uses it to execute a refund or change a status; the observable result is that the draft crosses the material's authorization boundary.
How to verify the setup
- Input the source text "Store order 20260309001 was out of stock on March 9, 2026; an appeal was submitted per platform requirements; the current status is pending review" and the draft sentence "The out-of-stock appeal for this order has been approved." Compare the draft conclusion against the material's status field. The condition is met when the draft keeps the pending status or marks the conclusion as unsupported.
- Input the source text "March 9, 2026 experience score 4.6" and the draft sentence "the experience score has recovered." Compare whether the draft gives a basis for recovery. The condition is met when the draft does not state a recovery conclusion.
- Input a hypothetical empty retrieval result explicitly labeled as hypothetical,
[], and check the downstream node's output design when no references exist. Compare whether the downstream treats the empty result as authorized execution. The condition is met when the downstream does not execute a refund or change a status. All three are paper reviews and were not actually run.