What the documents look like
On-demand retail fulfillment processes online orders by the minute, which separates it from periodic stocktaking and loss prevention. The daily materials in this category are live orders, picking lists, out-of-stock reports and delivery exception forms. Data arrives from the Douyin on-demand retail channel, the Meituan or JD Daojia back office, store inventory records and rider tracking. In the store service and internal training task, a multi-turn conversation has to handle substitution confirmation between picker and customer, out-of-stock explanations and delivery status questions. These details change with every order, so the current order state is the reference point. This page addresses only multi-turn dialogue and prompt configuration for this category and does not cover inventory counting or loss prevention.
The material scope, the answering task and the handling of missing evidence belong in the prompt. The model should not supply business facts that the material does not contain. Historical records need checking against the context actually carried into the turn, and a new session must be distinguished from a continued session. When a knowledge base supports continuous conversation, verify that references are resolved and that questions are expanded; short questions do not all qualify. Model context length and reply limits need testing against the actual material. Reply format follows the downstream consumer, and choosing json_schema requires filling in the corresponding format requirement and validating the returned structure.
A hypothetical paper exercise sample: the training material text reads "Line 3 of this order's picking list is a 500 ml bottled water, and the store inventory record shows two units of this specification are pickable; the customer note on the order states the water should be at room temperature." The draft sentence under review is "The bottled water in this order has been fully picked as the customer requested, and the delivery status is normal."
Constraints at the multi-turn prompts step
The AI configuration popup shows only the options the current model supports. When a model does not support multimodal recognition, that option is absent; when a model does not support thinking configuration, those options are absent as well. Memory turns control how many earlier rounds of chat the AI may reference at most. A larger value makes earlier dialogue easier to use but carries more content, which can raise consumption and affect response speed; a value that is too small may fail to use prior information. For customer service and knowledge base question answering, keeping a small number of history turns is usually sufficient.
The reply limit controls the maximum length of a single answer. When it is turned on, a slider restricts the model reply length. A setting that is too low may truncate the reply early, while a higher setting allows more complete content and may increase consumption. Temperature controls answer stability. Lower values give steadier answers suited to customer service, knowledge base question answering and rule-bound scenarios; higher values give more divergent answers suited to writing, brainstorming and creative content. Topp also controls reply randomness and overlaps with temperature. Adjusting temperature and Topp at the same time is generally not recommended. Stop sequences halt output when specified content appears, and ordinary chat usually does not need them. Reply format controls the answer structure; ordinary chat, customer service question answering and knowledge base question answering usually keep the default, and the format changes only when a downstream process needs to read fixed-format content. Hiding AI output means the generated content is not shown directly to the user but can still be passed to subsequent nodes through the AI reply output. Hiding AI thinking means the user sees only the final answer and not the reasoning process; during debugging, the switch can be turned off temporarily to observe intermediate thinking.
Configuration and implementation checks
| Configuration item | Hypothetical starting point for this exercise | Reason for the choice and comparison method |
|---|---|---|
| AI model | Select a model whose popup display shows it supports the required context length | Compare the model's stated maximum context against the combined length of the sample material plus the history turns |
| Memory turns | Hypothetical starting point of keeping a small number of history turns | Customer service material suggests keeping a small number; compare the turns actually carried into a continued session against the actual need |
| Reply limit | Hypothetical starting point of leaving the slider limit off | The sample reply is short; compare whether the reply is truncated with the limit on and off |
| Temperature | Hypothetical starting point of a lower value | Store service is a rule-bound scenario; compare answer stability across different values |
| Reply format | Hypothetical starting point of the default | No downstream process reads a fixed structure; if changed to json_schema, the format requirement must be filled in |
| Hide AI output | Hypothetical starting point of off | The customer needs to see the reply directly; compare whether subsequent nodes can consume the output once it is turned on |
These values are hypothetical starting points for this exercise and are not official default values. The configuration popup displays available items according to model capability, and unsupported items are not shown.
Three common errors
- Treating retrieval results as authorization. A knowledge base search that returns an out-of-stock report record does not mean the order has been approved for an out-of-stock substitution. The input is the
quoteListreturned by retrieval; compare whether it contains an authorization field; the observable result is that only text is returned with no approval status. - Treating an interaction node return as completed execution. Receiving a
Confirmoption fromuserSelectis only a user choice and not a completed pick. The input is theuserSelectOptionsreturned in the interactive response; compare whether any subsequent business write occurs; the observable result is that the interface returns only the selected value. - Treating a similarity score as an entity filter. The
scoreindicates relevance only and does not exclude other stores or unauthorized orders. The input is aquoteListcarryingscore; compare whether the returned records have been filtered by store identifier; the observable result is that score level is unrelated to correct ownership.
How to verify the setup
- For the hypothetical sample, input the original material text and the draft sentence, and compare whether the draft sentence contains "fully picked" and "delivery status is normal," which the original text does not state. The observable result should be that the statement is unsupported and requires verification.
- For a continued session, input a follow-up question under the same
chatId, and compare the history turns actually carried in against the memory turns setting. The observable result should show that the carried turns do not exceed the configured value. - For empty-result handling, input an empty list
[]returned by retrieval, and compare whether the downstream node handles it as having no references. The observable result should be that the downstream branch outputs on the missing-evidence path, without claiming that a fabricated rule search necessarily returns empty.
These are paper checks and were not actually executed. Retrieval does not carry authorization responsibility. Knowledge bases built on different vector models cannot be associated together; verify identifiers first and exclude those that do not match.