What the documents look like
Livestream violation appeals respond to penalties that have already been issued. The work reconstructs an evidence chain from the platform violation center, livestream replays, customer service records, and the product qualification library. Updates arrive on no fixed schedule; each new violation starts a fresh handling cycle. The recurring documents are the violation notice, the livestream recording, product qualifications, and the appeal statement. The most time-consuming steps are locating the violating segment, supplementing evidence, and drafting the appeal reasoning.
For copy drafting, two features of this material matter most. First, the notice records a specific session, product, quoted wording, and penalty status, and any draft sentence must stay inside that recorded scope. Second, qualifications carry their own subject, object, and validity range, and a valid qualification does not by itself establish that a penalty should be withdrawn. A multi-turn prompt therefore has to carry forward the material scope confirmed in earlier turns and state plainly how the model should behave when evidence is missing, rather than filling gaps with invented business facts.
Constraints at the multi-turn prompts step
Multi-turn drafting depends on session history. The conversation interface documentation states that when a non-empty chatId is passed, the session is taken automatically from the FastGPT database, the last item in the messages array is used as the user question, and the remaining messages are ignored. When chatId is not passed, no FastGPT context is used and the context is built entirely from the supplied messages. History that is actually carried in must be checked, and new sessions must be distinguished from continued sessions.
Model and length settings need checking against the material. The AI configuration documentation describes maximum context as the length a single request can reference, a reply cap that can truncate output when set too low, and a temperature that produces steadier output at lower values. Reply format stays at default unless a downstream step needs to read a fixed structure; selecting json_schema requires filling in the corresponding format requirement and verifying the returned structure. Thinking budget is a reasoning budget and is not a citation token ceiling. Multimodal recognition depends on model capability, and unsupported types are filtered again before the request is sent.
Assumed paper exercise sample. Material text for comparison: "The violation notice states: for the session on 12 March 2025, the explanation segment for product A contained the wording 'lowest price on the entire internet'; the penalty status is product broadcast ban; the appeal deadline is 19 March 2025." "The product qualification library records: the filing subject for product A is a supply chain company; the filing validity runs to 31 December 2025." Draft sentence to be judged: "Product A has obtained complete filing, and this penalty should be withdrawn." This draft sentence merges filing validity with penalty withdrawal into one fact. Filing status does not constitute a basis for withdrawal, so the sentence is unsupported.
Configuration and implementation checks
| Configuration item | Assumed starting point for this exercise | Reason and comparison method |
|---|---|---|
| AI model | A conversational model that supports long context | Compare against appeal material length; check whether maximum context covers the violation notice and qualification text |
| Memory turns | Set to cover the notice, qualification, and draft-sentence turns | Compare against history actually carried into a continued session; confirm the earlier material scope is not truncated |
| Reply cap | Set to hold a complete appeal statement paragraph | Compare against draft-sentence length; observe whether truncation occurs early |
| Temperature | Set low | Compare against rule-defined scenarios; observe whether the draft sentence cites material wording steadily |
| Reply format | Keep default | Compare against the downstream step; switch to json_schema only when a fixed structure must be read |
| Multimodal recognition | Decide whether to enable image recognition by model capability | Compare against screenshot input from the livestream recording; check whether unsupported types are filtered before sending |
Three common errors
- Treating retrieval similarity as a permission or entity filter. A similarity score does not exclude unrelated businesses or unauthorized records, and retrieval does not carry authorization duties. Check input as one continued-session request with chatId; compare the returned quoteList against the material scope; observe whether records from other sessions are mixed in.
- Writing filing validity as a basis for penalty withdrawal. The draft sentence "has obtained complete filing, and the penalty should be withdrawn" merges two different facts. Check input as that draft sentence together with the filing text; compare whether subject, object, and status occupy the same field; observe whether an unrecorded withdrawal conclusion appears.
- Treating an interactive node's display configuration as a completed business action. The interactive response contains only type and params, and a pending review does not show that operating permission is absent. Check input as one interactive response with detail=true; compare whether internal fields such as entryNodeIds are returned; observe whether the display configuration is misread as completed approval.
How to verify the setup
- Input the violation notice text and the draft sentence from the assumed sample; compare whether the draft sentence cites only the session, product, wording, and penalty status recorded in the notice; observe whether an unrecorded withdrawal conclusion appears in the output. The condition is met when the draft sentence preserves the evidence gap.
- Input one new-session request and one continued-session request with chatId; compare the context scope actually carried in each time; observe whether the continued session uses only the last messages item as the user question. The condition is met when history turns match the material scope.
- Input one follow-up containing an image link; compare model multimodal capability against the link-extraction condition; observe whether unsupported types are filtered before the request. The condition is met when sent content matches model capability and unsupported types are not sent.