What the documents look like
Douyin storefront operations center on product cards and the store experience score, and they do not depend on the pacing of a single livestream session. The brief lists daily and per-order update frequencies, which means the experience score detail, shipping violation appeal, and store announcement materials keep producing new versions during store service and internal training work. The multi-turn prompts step has to treat the product qualification page, experience score detail, shipping violation appeal, and store announcement as a checkable document scope, and keep internal training answers inside that scope. Fields the brief does not provide stay unknown, and a retrieval hit does not grant authorization.
Constraints at the multi-turn prompts step
The store service and internal training prompt needs to state the document scope, the answering task, and how to handle missing evidence. For history, a new session and a continued session accept different input. When chatId is not passed, context is built entirely from the supplied messages. When a non-empty chatId is passed, FastGPT reads the session from its database and uses only the last item of the messages array as the user question, while the remaining messages are ignored. For question refinement, continuous knowledge base conversation needs coreference handling checked, and short questions do not automatically qualify. For model and length, the supported context and the reply ceiling need to be checked. The reply format is chosen by what the downstream step consumes; when json_schema is selected, the corresponding format requirement is filled in and the returned structure is verified. Retrieval and similarity carry neither permission filtering nor entity isolation.
Hypothetical paper exercise sample: inside a Douyin store merchant internal training knowledge base, the source text of a product qualification page reads "The operating entity of this store is XX Apparel Firm. The qualification for the knitwear category is valid until March 31, 2026, and the current status is submitted and pending review." A proposed draft sentence reads "This store's knitwear qualification has passed review and can be sold with confidence." The request to be judged is: write this draft sentence into the prompt as the standard reply for training questions.
Configuration and implementation checks
The table below is built around that sample and lists officially documented settings or implementation checks relevant to this feature and the primary task. The values are starting points for this hypothetical exercise, used for comparison against the sample material, and are not official defaults.
| Configuration item | Starting point for this hypothetical exercise | Reason and comparison method |
|---|---|---|
| AI model | A model that supports knowledge base question answering | Check whether the model's maximum context and tool-calling capability cover the length of the sample material |
| Memory rounds | Keep a small number of history rounds | Customer service and knowledge base applications keep a small number, compared against the single-turn sample question |
| Reply ceiling | Enabled with a moderate length | The training reply must state the status completely; compare against the draft sentence to see whether truncation occurs |
| Temperature | On the low side | Rule-defined scenarios need stability; compare against the consistency of the status word "pending review" in the sample |
| Reply format | Keep default | The sample has no fixed-structure downstream step, so json_schema is not selected here |
| Hide AI output | Off | Training answers need to be shown directly, compared against the display requirement of the draft sentence |
Three common errors
- Writing "passed review" where the material says "pending review." The sample material states "submitted and pending review," while the draft sentence states "passed review," so the same field, qualification status, carries mutually exclusive values. The check compares the status word in the source text with the status word in the draft sentence.
- Treating a retrieval hit as authorization. A retrieval result returning this qualification page does not mean the qualification is effective or can be promised externally. The check compares the retrieval result against the effective range and status in the material, and records the gap when authorization evidence is absent.
- Using a similarity score to exclude unrelated records. Similarity is a ranking basis, not an entity or permission filter. The check compares the business entity in the returned record against the sample entity, excludes mismatches, and does not infer runtime results.
How to verify the setup
- Input the sample source text and the draft sentence, compare "submitted and pending review" with "passed review." If the two status words are mutually exclusive, the draft sentence is judged unsupported and must be rewritten to match the material's status wording.
- Input a question that goes beyond the sample document scope, compare whether the reply introduces business facts not stated in the material. If it does, record this as outside the document scope, and add the missing-evidence handling to the prompt.
- Use an explicit paper input of an empty result list to check the downstream design, compare how the downstream node behaves when no references are present, and observe whether any automatic business action not described in the material appears; if one appears, record it as needing separate verification.