What the documents look like
Channel receivables collection happens after credit sales, and its core concerns are the credit line and cash recovery. The working materials come from the finance system, bank receipts, distributor orders and sales contracts. Day-to-day handling covers the accounts receivable aging table, dunning letters, credit agreements and shipment freeze notices. An aging table records the distributor name, shipment batch, amount, overdue days and remaining credit balance. A dunning letter records the letter number, issue date and the agreed repayment arrangement. A credit agreement records the credit ceiling, payment term and guarantee conditions. A shipment freeze notice records the frozen object and the scope in which it takes effect. These materials update weekly or monthly.
This page addresses one task: drafting product and marketing copy. That task extracts checkable facts from the materials above and writes them as product descriptions or marketing sentences directed at distributors. The role of multi-turn prompts at this step is to keep the model inside the material scope across consecutive turns, so that aging figures, credit status or freeze objects do not enter promotional sentences.
Constraints at the multi-turn prompts step
Multi-turn dialogue carries earlier messages forward. The number of remembered turns is governed by the memory-turn setting. A larger value makes earlier content easier to reference, brings in more content, and may raise consumption and affect response speed; a smaller value leaves earlier information unused. When drafting product copy, if earlier turns discussed aging figures, later turns may carry those figures into a promotional sentence, so the context actually carried in needs checking.
The prompt needs to state the material scope, the answering task and how to handle missing evidence. A promotional point not recorded in the materials must not be added by the model. The reply format follows what the downstream step consumes; selecting json_schema requires filling in the corresponding format requirement and checking the returned structure.
Retrieval locates source text; it does not perform permission or entity isolation. A relevance score is not a filter on distributor identity, and a retrieval result is not authorization. Collections built on different vector models cannot be associated together; the identifiers need checking first and non-matching ones excluded.
Hypothetical paper exercise sample
The following is constructed for this paper exercise and comes from no real customer or real record.
Material text one (aging table excerpt): "Distributor A, shipment batch A-07, receivable amount 120,000 yuan, overdue 45 days, remaining credit balance 30,000 yuan."
Material text two (credit agreement excerpt): "The credit ceiling under this agreement is 200,000 yuan, the payment term is 60 days, and the guarantee method is a deposit."
Material text three (dunning letter excerpt): "Letter number C-2024-018, issue date 10 May 2024, agreed repayment arrangement in two installments."
Draft sentence to be judged: "Distributor A has excellent credit and can enjoy a longer payment term and a higher credit line."
Basis for judgment: the material text does not record a credit assessment of Distributor A, nor any approval record for extending the payment term or raising the credit line. The phrases "excellent credit," "longer payment term" and "higher credit line" in the draft sentence have no support.
Configuration and implementation checks
| Configuration item | Hypothetical starting point for this exercise | Reason and comparison method |
|---|---|---|
| AI model | A model that supports tool calling and long context | Compare with a model that does not support long context; check whether the maximum context value shown in the dialog covers the material length |
| Memory turns | Set to 3 turns | Compare with 1 turn and observe whether aging figures from earlier turns enter the promotional sentence |
| Temperature | Set to a lower value | Compare with a higher value and check whether the same draft sentence stays stable |
| Reply cap | Enabled at a medium length | Compare with a shorter setting and check whether the draft sentence is truncated early |
| Reply format | Left at default | Compare with json_schema; switch only when a downstream node needs to read a fixed structure |
| Hide AI thinking | Disabled during debugging | Compare with enabled and check whether the final answer shows only the draft sentence |
These values are starting points for this hypothetical exercise and are not official defaults.
Three common errors
- Writing aging figures or the credit balance directly into a product promotional sentence. The amounts and day counts in the material text are internal financial information; if a draft sentence cites them, it needs checking against an explicit basis for external disclosure.
- Treating a retrieval relevance score as a filter on distributor identity or permission. A relevance score reflects text matching only and is not an authorization judgment. Collections built on different vector models cannot be associated together; the identifiers need checking first.
- Adding a credit assessment or a line adjustment when the material does not record one. The phrases "excellent credit" and "higher credit line" in the draft sentence have no corresponding entry in the material text and are unsupported content; the evidence gap stays open.
How to verify the setup
- Input material texts one through three above, together with the draft sentence to be judged. Compare "excellent credit," "longer payment term" and "higher credit line" in the draft sentence against what the material text records. The condition is met when the draft sentence does not contain these three items, or marks them explicitly as unsupported.
- Input the same draft sentence and request it twice, once with memory turns set to 1 and once set to 3. Compare whether the amount or overdue days from material text one appear in either output. The condition is met when neither output contains those figures.
- Input the dunning letter excerpt from material text three and request a marketing sentence reminding the distributor to repay. Compare whether the output turns "agreed repayment arrangement in two installments" into something already executed or completed. The condition is met when the output states only the agreed arrangement itself and does not claim it has been carried out.
The above are expected results and were not actually run. Fields not recorded in the materials remain unknown; a pending review status is not an approval, and a quotation validity period does not indicate a supply period.