What the documents look like
Brand livestream operations differ from influencer-hosted sessions because the brand carries its own scripts, inventory and after-sales work rather than relying on creator commissions. In a store-service and internal-training setting, multi-turn dialogue has to serve two audiences at once: viewers asking follow-up questions about product explanations and campaign rules, and store staff checking script wording and shift arrangements. The working documents are livestream scripts, product explanation cards, shift schedules and livestream reviews. Data comes from the Douyin Shop compass, Juliang Qianchuan, livestream comments and transaction details, refreshed per session and per hour. These documents define what a prompt may quote and what it must leave unwritten. Multi-turn dialogue keeps a follow-up inside the same session's wording; the prompt limits the answer range and states the gap when evidence is missing.
Constraints at the multi-turn prompts step
Multi-turn dialogue depends on history, and the context actually carried in needs checking. A new session and a continued session accept different input. In the chat interface, when chatId is not passed, the FastGPT-provided context function is not used and the context is built entirely from the passed messages. When chatId is a non-empty string, the session is taken from the FastGPT database automatically, the last item in the messages array is used as the user question, and the remaining messages are ignored. The memory round count controls how many earlier rounds of chat the AI answer may reference at most; a larger value makes earlier dialogue easier to reference and also carries in more content. The prompt needs to state the material scope, the answering task and the handling of missing evidence, so the model does not write in business facts. Question optimization is checked for referent resolution and question expansion only when continuous knowledge-base dialogue is needed; it is not assumed to fit every short question. Retrieval does not carry an authorization role, and a similarity score is not a permission filter.
Hypothetical paper exercise sample: the source material reads "This session's explanation card states that a certain product joins the livestream campaign price from 20:00 to 22:00 on that day, and inventory is subject to the floor controller's on-site announcement; the shift schedule states that group A handles explanations during that period." The proposed copy sentence reads "This product joins the campaign price all day, and inventory is sufficient." The request to be judged reads "Please confirm whether the campaign period and inventory wording for this product can be used externally."
Configuration and implementation checks
Configuration centers on checking the sample, without copying database internals into required items. The values are starting points for this hypothetical exercise, with a stated reason and a comparison object.
| Configuration item | Hypothetical starting point for this exercise | Checking method |
|---|---|---|
| AI model | Choose a model shown in the popup as supporting the needed context length | Compare with the text length of the explanation card and shift schedule, and confirm the maximum context is sufficient |
| Memory round count | Hypothetical starting point of keeping a small number of history rounds | Compare with the documented note that customer-service and knowledge-base Q&A applications usually keep a small number of history rounds |
| Reply limit | Turned on and set according to answer-length needs | Compare with the explanation length needed for the card and review text, and observe whether truncation occurs |
| Temperature | Hypothetical starting point of low | Compare with the documented note that customer service, knowledge-base Q&A and rule-clear scenarios suggest a lower value |
| Reply format | Keep default for ordinary Q&A | Change to json_schema only when a downstream flow needs to read fixed-format content, and fill in the format requirement |
| Multimodal recognition | Decide whether to enable image recognition based on model capability | Compare with the capability shown in the model popup; unsupported types are not sent |
Three common errors
- Treating a similarity score as a permission or entity filter. The score returned by retrieval indicates relevance only; it does not exclude unrelated businesses or unauthorized records, and retrieval itself is not authorization.
- Treating the display configuration returned by an interactive node as already executed. The interactive returned to an external caller contains only type and params; internal runtime fields such as entryNodeIds, memoryEdges, nodeOutputs and nodeResponseId are not returned. A form collecting values does not mean business verification, approval, a database write or external synchronization has completed.
- Treating an unstated field as a negative conclusion. The material states the campaign period as 20:00 to 22:00 and does not state all day, so a copy sentence claiming all-day participation is unsupported. Inventory is written as subject to the floor controller's on-site announcement and is not written as sufficient, so claiming sufficient inventory is likewise unsupported.
How to verify the setup
- Input the sample source material and the proposed copy sentence, and compare the campaign-period field: the material gives 20:00 to 22:00, the proposed sentence gives all day, and the two take mutually exclusive values on the same field, so the judgment is unsupported. The expected observation is that the prompt output points out the period inconsistency and does not pass it through directly.
- Input the sample source material and the inventory wording, and compare the inventory field: the material gives subject to on-site announcement, the proposed sentence gives sufficient inventory, and the material does not state sufficiency, so the judgment is pending verification. The expected observation is that the output keeps the gap and does not write in an inventory quantity.
- Input an empty-result continuation scenario, using the explicit paper input [] to check the downstream design, and compare whether downstream handles it as an empty reference. The expected observation is that downstream does not treat the empty result as containing content, and no claim is made that a fictional rule retrieval necessarily returns [].