What the documents look like
Semi-managed sellers maintain overseas inventory and ship from foreign warehouses. That work separates this category from supply-only arrangements, and it shapes the material a copy-drafting session can rely on. The working papers for this task are the product declaration form, the overseas inventory sheet, the price declaration, and the platform fulfillment order. Each carries a different kind of fact: the declaration form states attributes, the inventory sheet states sellable status, the price declaration states the basis for a listed price, and the fulfillment order states fulfillment status. These materials update daily or per order, so a drafting session must work from the version in hand and record which version it used.
Because the source materials differ in what they establish, a prompt has to name the scope of the material it may cite, the drafting task, and the treatment of missing evidence. The model must not fill business facts that the materials do not state. A multi-turn session carries earlier turns into later context, so the drafting step has to check which turns were actually carried in and distinguish a new session from a continued one. The context length and reply ceiling supported by the chosen model determine whether a long material set combined with follow-up questions can hold together. Reply format follows the downstream consumer; when json_schema is selected, the corresponding format requirement must be supplied and the returned structure verified.
Hypothetical drill sample. Material text for comparison: "Product declaration form: the subject is a cross-border supplier, the object is a cotton short-sleeve top, the status is pending review, and the effective scope is the summer 2025 batch. Overseas inventory sheet: the same object has a sellable quantity of 300 units in the foreign warehouse, updated 1 June 2025. Price declaration: the basis for the listed price of this object is procurement cost plus freight, and the quote validity period is 1 June 2025 to 30 June 2025." Draft sentence to be assessed: "This cotton short-sleeve top has passed review, the summer batch can be supplied steadily, and the price is valid long term."
Constraints at the multi-turn prompts step
The drafting step sits inside a conversation, so the material scope, the task statement, and the missing-evidence rule belong in the prompt rather than in a later correction. History is genuinely carried into context; the operator checks the turns actually included and separates a fresh session from a continued one. Where a knowledge base supports continuous conversation, coreference removal and question expansion are checked, and short questions are not assumed to fit every case. Model context length and reply settings are checked against the long-material-plus-follow-up combination.
Reply format is chosen by what consumes the output. Selecting json_schema requires filling in the corresponding format requirement and verifying the returned structure. Recall and relevance scores carry no permission or entity isolation, so a similarity value cannot stand in for authorization or for confirming which business an object belongs to. Libraries built on different embedding models cannot be associated together; identifiers are checked first and non-matching libraries are excluded, with the actual observation recorded.
Configuration and implementation checks
| Configuration item | Hypothetical starting point for this drill | Reason and comparison method |
|---|---|---|
| Memory turns | A small number of turns | Drafting needs the previous material line; compare against a single-turn result without history |
| Reply ceiling | A starting point that can hold a full copy block | Compare against truncation, observing whether output ends early |
| Temperature | A low starting point | Compare against a higher setting for divergence from the material |
| Reply format | Default | Compare against a json_schema returned structure, choosing by downstream consumer |
| Thinking configuration | Default | Compare against a direct answer with thinking off, choosing by question complexity |
| Hide AI output | Off | Compare against on, where only a later node handles the result |
Three common errors
- Writing "pending review" as "has passed review." The material states the status is pending review, while the draft sentence asserts review has passed. The input is the declaration form text, the comparison object is the status field, and the observable result is that the two values disagree.
- Writing the quote validity period as a supply term. The material states the quote validity period is 1 June to 30 June, while the draft sentence says the price is valid long term. The input is the price declaration text, the comparison object is the validity period field, and the observable result is that the period range has been widened.
- Treating a similarity score as authorization or entity filtering. The score returned by retrieval is relevance only. The input is the retrieval result, the comparison object is permission and entity ownership, and the observable result is that a high or low score says nothing about whether access was granted.
How to verify the setup
- Input the hypothetical material text and draft sentence above, then compare whether the status and period in the draft sentence share the same subject, the same period, and the same field as the material. A contradiction is recorded only when the same field carries mutually exclusive values; where the material does not state a value, the result is recorded as unsupported.
- Input the draft sentence above together with the handoff request, then compare whether the carried history turns cover that material. Observe whether the continued session cites the previous turn; where the material was not carried in, record an evidence gap.
- Input a paper empty result, then compare whether the downstream handoff design handles empty references. Observe whether business facts are filled in; where the branch is unhandled, record it as pending.
Semantic recall and relevance do not carry permission or entity isolation. Libraries across different embedding models cannot be associated together; identifiers are checked first and non-matching libraries excluded, with the actual observation recorded.