What the documents look like
Promotion enrollment and price management centers on price, inventory, and campaign rhythm rather than on advertising-wording review. The working materials are the campaign enrollment form, the promotional price approval form, the inventory lock table, and the campaign review table. Source systems named in the brief are the Tmall marketing backend, the JD campaign backend, ERP inventory, and historical transaction prices. Typical roles are platform campaign operations, brand sales planning, and finance business partners. Errors here produce campaign losses, removal of resource slots, and price complaints.
For copy drafting, these documents usually carry only the campaign name, the enrollment window, the scope of participating products, the promotional price basis, and inventory status. Whether a proposed promotional sentence holds depends on whether those fields agree on the same subject, the same period, and the same field. The brief gives a weekly and per-campaign update frequency. This page does not depend on batch synchronization or transaction triggers, so that cadence is not expanded here.
Constraints at the multi-turn prompts step
Multi-turn dialogue is where history determines what context the model actually carries. In the documented chat interface, when chatId is absent the system does not use FastGPT-provided context and builds context entirely from the supplied messages. When chatId is a non-empty string, the system takes the session from the FastGPT database and uses the last item in the messages array as the user question while ignoring the rest. Prompts need to state the material scope, the answering task, and how to behave when evidence is missing, so the model does not write in business facts that the materials do not contain. Question optimization is used to check reference resolution during continuous knowledge-base dialogue; it is not assumed to apply to every short question. Model and length settings need their context and reply ceiling checked, and long documents combined with follow-up questions need separate testing. Reply format follows the downstream consumer; choosing json_schema requires filling in the corresponding format requirement and verifying the returned structure. Semantic recall and relevance scores do not perform permission or entity isolation, and a same-vector-model requirement inside a retrieval library does not extend to external systems.
Hypothetical drill sample, clearly labelled as such: the material text reads "Campaign enrollment form: June 2025 major promotion, store A, product X, enrollment window June 1 to June 18, promotional price approval form number PA-0618, approval status pending review; the inventory lock table shows product X locked at 500 units." The draft sentence to be judged is "Product X has passed approval, the promotional price is in effect, inventory is locked, order with confidence." The comparison checks subject, period, and field: the approval status field reads pending review, which does not agree with "has passed approval"; the inventory lock table records only a locked quantity and does not state an effective status; the draft sentence treats pending review as passed and is therefore unsupported.
Configuration and implementation checks
| Configuration item | Relevant value or check on this page | Relation to the sample |
|---|---|---|
| AI model | Select a model whose context length and multimodal ability fit the material | Long materials with follow-ups need sufficient context |
| Memory turns | Keep history turns as needed; service-style use keeps few | The sample is a continuous follow-up and needs prior material |
| Reply ceiling | Adjust to copy length; too low truncates | The draft sentence is short, so a default is likely enough |
| Temperature | Copy work may go higher; rule-bound work stays lower | This task restates material, so a lower starting point fits |
| Reply format | Choose json_schema when downstream reads fixed structure, and fill the format requirement | If downstream reads the approval status field, a fixed structure is needed |
| Multimodal recognition | Enable image or audio recognition when the model supports it | Material containing screenshots needs image input support |
The values above are hypothetical starting points for this exercise, not official defaults. The lower temperature starting point is chosen for comparison against the stable orientation described for service and knowledge-base question answering. The reply ceiling stays at default and is confirmed against the draft sentence length to check that no truncation occurs.
Three common errors
- Treating pending review as passed approval. In the sample the approval status field reads pending review, while the draft sentence says "has passed approval." The two values are mutually exclusive on the same field, which is a contradiction. To check, input the material text and the draft sentence, compare the approval status field, and treat "pending review" and "has passed" pointing at the same approval form as the error.
- Treating a locked quantity as an effective status. The inventory lock table records only a locked quantity of 500 units and does not state effectiveness, while the draft sentence says "inventory is locked." To check, compare the inventory lock table field with the draft sentence; where the material does not state an effective status and the draft asserts it, the sentence is unsupported.
- Treating semantic recall as permission filtering. A relevance score does not perform entity or permission isolation, and a same-vector-model requirement inside a retrieval library does not extend to external systems. To check, verify the dataset identifier and the vector model identifier, exclude those that do not match, and do not run a cross-model retrieval comparison for missed recall.
How to verify the setup
- Input the sample material text and the draft sentence, compare the approval status field, and when the material reads "pending review" while the draft reads "has passed approval," judge it a contradiction. The output should point out that the field does not agree and should not write in an approval result.
- Input the inventory lock table fields and the draft sentence, compare the effective status, and when the material does not state effectiveness while the draft asserts it, judge it unsupported. The output should preserve the evidence gap and should not fabricate an effective clause.
- Input a paper input of
[]for empty-result handling and check whether the downstream design handles an empty list, without claiming that a fictional rule retrieval must return empty. Cross-model records cannot be jointly associated; verify identifiers first, exclude those that do not match, and record what is observed rather than inventing a run result.
This page addresses only platform promotion enrollment and price management, product and marketing copy drafting, and multi-turn prompts. Whether a specific promotional sentence is supported by the materials is not turned into an enterprise identity certification tutorial. The pending-request status is not an approval, an offered option is not an execution, and an unsigned termination is not a completed termination.