What the documents look like
Livestream after-sales work clusters after a single traffic session. Refund requests, reshipment orders and the livestream order list arrive in the same window, and a shared script template carries one explanation of what the host promised. Copy drafting therefore is not isolated writing. The drafting step has to state the order status, refund status and reshipment status as the material scope, then let the model produce only sentences that can be checked against those materials. The brief gives an update frequency of after each session and daily, which affects the checking time point; it does not establish any batch synchronization or real-time trigger.
The four document types named in the brief are the livestream order list, refund applications, reshipment orders and script templates. A drafting prompt that does not name which of these supplies the status field leaves the model free to fill gaps with plausible business facts. The relevant FastGPT settings are the AI model, memory turns, reply limit, temperature, reply format and multimodal recognition. Memory turns control how many earlier rounds the answer may reference; a larger value brings in more content and can raise consumption and slow the response, while a smaller value may fail to use earlier information. The reply limit controls the maximum length of one answer, and a low setting can cut the reply short. A low temperature suits customer service, knowledge-base question answering and rule-bound scenarios. The reply format follows what the downstream step reads; choosing json_schema requires filling in the matching format requirement.
Constraints at the multi-turn prompts step
Multi-turn dialogue carries earlier text into later requests. The drafting prompt must state the material scope, the answering task and how to handle missing evidence, so the model does not write business facts that the materials do not carry. The following paper exercise is labelled as a hypothetical test case and was not run against any real record.
Hypothetical test case. The material text supplied for checking has three sentences. Sentence one: "Livestream order A was submitted for a refund on 5 March, and the current status is pending review." Sentence two: "The product on that order is a summer short-sleeve item, and the host stated in the 4 March livestream that this style does not support the seven-day no-reason return." Sentence three: "Warehouse reshipment order B corresponds to order A, the reshipment reason is a wrong size shipped, and the current status is pending outbound." The draft sentence to be judged is: "The refund for order A has been approved, and the seven-day no-reason return is supported." The subject is order A, the objects are the refund status and the return rule, the states are pending review and not supported, and the effective scope is the application submitted on 5 March. The draft sentence and the material text take mutually exclusive values on the same subject, the same period and the same field, so the draft sentence is unsupported.
A second condition concerns the context actually carried into the request. The chat interface documentation states that when chatId is a non-empty string, the system takes the session from the FastGPT database and uses the last item of the messages array as the user question, ignoring the rest. When chatId is not passed, no FastGPT context is used and the full context comes from the supplied messages. A drafting check has to record which of these two situations applies, because it changes what earlier text the model can see. The same documentation notes that model, temperature and similar fields passed in the request body have no effect, since the orchestration decides them.
Configuration and implementation checks
The table below lists documented settings and implementation check items tied to this page's documents. The values in the middle column are starting points for this hypothetical exercise, with the reason for choosing them and the value they are compared against.
| Configuration item | Hypothetical starting point for this exercise | Reason and comparison object |
|---|---|---|
| AI model | Select a model whose displayed context length covers the material text | Compare against the length of the material text and whether it contains images; model capability follows the configuration dialog display |
| Memory turns | Keep a small number of history turns | Compare against the usual practice for customer service and knowledge-base question answering; a larger value brings in more content |
| Reply limit | Open the limit and set a length that holds a complete explanation | Compare against the length the draft sentence needs; a low setting can cut the reply short |
| Temperature | Low | Compare against the common choice for customer service and rule-bound scenarios; keep the default when uncertain |
| Reply format | Keep the default for ordinary chat; choose json_schema and fill the format requirement when a fixed structure is needed | Compare against whether a downstream step reads a fixed format |
| Multimodal recognition | Enable the matching type only when the model supports it and the material contains images | Compare against model capability; unsupported types are not sent to the model |
The documentation states that the configuration dialog shows only the options the current model supports. A model without multimodal recognition does not offer that option, and a model without thinking configuration does not show the related options. The documentation also states that ordinary document files are not sent to the LLM as multimodal input; document content still has to be converted to text through file parsing. Media links inside a user question are only attempted when the extract-multimodal-files option is on, only when the user question is under 500 characters, and at most four media links are processed in one attempt. These conditions belong to the documented behaviour and are not settings this page invents.
Three common errors
- Treating "refund approved" in the draft sentence as supported by the material. The input is sentence one of the material text, "the current status is pending review". The compared field is the refund status. The observable result is that pending review and approved do not match, so the draft sentence is unsupported.
- Reading "does not support the seven-day no-reason return" as support. The input is sentence two of the material text. The compared field is the return rule. The observable result is that not supported and supported do not match.
- Treating the pending-outbound status of reshipment order B as evidence that the refund for order A is complete. The input is sentence three of the material text. The compared fields are the reshipment status and the refund status. The observable result is that pending outbound and complete are two different fields, and one cannot stand in for the other.
How to verify the setup
- Input the three sentences of the material text together with the draft sentence, compare the refund status field, and observe whether pending review and approved do not match. When they do not match, the draft sentence is unsupported and the evidence gap is kept.
- Input sentence two of the material text, compare the return rule field, and observe whether not supported and supported do not match. When they do not match, the draft sentence is unsupported.
- Input sentence three of the material text, compare the reshipment status and the refund status, and observe whether pending outbound and complete do not match. When they do not match, the reshipment order status cannot be used to infer that the refund is complete.
All three checks are paper comparisons. No test, deployment, approval or refund is claimed to have occurred. Where the material text does not carry a field, the field stays unknown and the gap is recorded rather than filled.