What the documents look like
Presale group-buy operations collect orders first and organize procurement and fulfillment afterward. This ordering affects copy drafting, because the product copy and marketing copy depend on the presale product table, the group-order result, the procurement plan, and the cut-off notice. These materials update daily or per session, and the specifications, quantity ranges, cut-off times, and fulfillment wording that appear in drafted copy need to match the materials for that same session.
Multi-turn dialogue supports drafting by letting a requester ask follow-up questions and revise wording across turns. Conversation history carries earlier context into later turns, so the material version cited in each turn needs to correspond to the same session. The documents that feed drafting are the presale product table, the group-order result, the procurement plan, and the cut-off notice. Where those materials leave a specification, a timing commitment, or an operational promise unstated, the model is not to supply it as a business fact.
Constraints at the multi-turn prompts step
Prompts need to state the material scope, the drafting task, and the handling of missing evidence. When a session continues, the system takes the conversation from the database by chatId and uses the last item in the messages array as the user question; the remaining messages are ignored. When a new session does not pass a chatId, context is built entirely from the submitted messages. Question optimization targets continuous knowledge-base dialogue, used for reference resolution and question expansion; whether a short question applies is checked against the actual configuration rather than assumed.
On model and length, the maximum context and reply limit that the model supports need to be checked, and long-document plus follow-up combinations tested. Reply format follows what the downstream step will consume; selecting json_schema requires filling in the corresponding format requirement and verifying the returned structure. Multimodal recognition depends on model capability, and when the model supports it, image, audio, or video input can be enabled as needed.
Hypothetical drill sample: the source material reads, "The presale product for this session is bulk potatoes, specification is 5 kg per bag, cut-off time is 18:00 on the same day of this session; the group-order result is based on mini-program orders, and the procurement plan is compiled after cut-off." The drafted sentence reads, "Potatoes this session are 5 kg per bag, cut-off is today at 18:00, and procurement can be arranged as soon as the order is placed." The task is to judge whether "procurement can be arranged as soon as the order is placed" is supported by the material. The material states that procurement is compiled after cut-off; it does not state that procurement can be arranged immediately upon ordering. That phrase is unsupported.
Configuration and implementation checks
| Configuration or check item | Purpose on this page | Hypothetical trial starting point and comparison method |
|---|---|---|
| AI model | Carries copy drafting and follow-up answers | Choose a model shown in the dialog as supporting the needed context length; compare against the material length the drafted sentence requires |
| Memory turns | Controls how many prior turns are carried in | Hypothetical starting point is keeping a small number of history turns; compare against drafting from this turn's material only |
| Reply limit | Limits single-reply length | Hypothetical starting point is a limit that fits the complete drafted sentence; compare against truncation |
| Temperature | Controls wording stability | Hypothetical starting point is low; compare consistency of repeated drafting from the same material |
| Reply format | Feeds downstream reading | When a fixed structure is needed, select json_schema and fill in the format requirement; compare against the returned structure |
| Multimodal recognition | Reads product images or posters | Enable as needed when the model supports it; compare input content with the setting off |
These values are hypothetical trial starting points. The reason for choosing them is consistency with drafting that needs stable, checkable results; the comparison object is the actual output from the same material.
Three common errors
- Writing a fulfillment promise into the drafted sentence that the material does not state. Input is the source material above and the drafted sentence. Compare "procurement can be arranged as soon as the order is placed" against "the procurement plan is compiled after cut-off." The material only states compilation after cut-off and does not state immediate arrangement upon ordering, so that phrase is unsupported.
- Treating all messages as participating when a session continues. Input is a request with a chatId and multiple messages. Compare the context actually carried in. The system uses only the last item as the user question; the rest are ignored.
- Treating a relevance score as a permission or entity filter. Input is a score returned by retrieval. Compare its definition. Score indicates similarity and does not carry business authorization or entity isolation.
How to verify the setup
- Input the source material above and the drafted sentence. Compare whether the time and fulfillment wording in the drafted sentence can be matched to a field in the source. The expected result is that "procurement can be arranged as soon as the order is placed" is marked unsupported, while the cut-off time and specification correspond.
- Input a continuing-session request with a chatId and multiple messages. Compare which user question is actually used for the answer. The expected result is that only the last item is adopted and the remaining messages are ignored. If the result differs, check the chatId and how the messages are organized.
- Input a paper empty result [] together with the downstream design. Compare how the downstream step outputs when the reference is empty. The expected result is that the downstream step handles the empty reference and does not produce a business conclusion without a source. Whether retrieval returns empty is recorded from actual observation and is not guaranteed.
The primary task here is whether a specific drafted sentence is supported by the supplied material. This page does not cover enterprise identity verification, report analysis, or any other task.