Trade returns and claims: multi-turn prompts for copy drafting

Review multi-turn prompts for copy drafting in Trade returns and claims. Check document scope, failure paths and source evidence through controlled tests.

What the documents look like

Trade returns and claims handle reverse logistics and liability determination after export, which separates this category from ordinary export declaration work. The materials available for drafting marketing or product copy come from customer emails, quality inspection reports, customs systems, and insurance claim systems. In practice, the checkable documents are the return agreement, the claim letter, the quality report, and the return customs declaration. Each document carries a different subject, object, status, and valid scope: the agreement states the return scope, the quality report states the defect determination, and the customs declaration states the declaration status. A promotional sentence that touches a return reason or a compensation conclusion needs a matching statement inside these materials before it can be used.

A second practical fact is that these documents are not updated on a fixed schedule; each case produces its own set. Drafting therefore relies on the specific case file rather than a standing template. A third fact is that the three most time-consuming steps are confirming the return reason, handling the return declaration, and submitting claim evidence. Each step produces text that later drafting may quote, so the drafting stage inherits the evidence limits of those steps.

Constraints at the multi-turn prompts step

Multi-turn dialogue depends on history being carried into context. Per the documented AI settings, the memory turns setting controls how many earlier chat rounds the AI may reference; a value that is too small may fail to use earlier information. When a drafting task requires successive follow-up questions, the actual history carried in should be checked, and the input of a new session should be distinguished from the input of a continued session. The prompt needs to state the material scope, the answering task, and how to handle missing evidence. A similarity score in retrieval results is not a permission or entity filter, and retrieval itself does not constitute authorization. Response format follows downstream consumption; selecting json_schema requires filling in the corresponding format requirement and checking the returned structure.

Hypothetical paper exercise sample: the material text is "The return agreement states: this batch of goods is returned due to surface scratches, return quantity 200 pieces, signing date March 5, 2024." "The quality report states: 20 pieces sampled, of which 4 pieces have surface scratches." "The return customs declaration states: declaration status is released." The draft sentence to be judged is "All goods in this batch have serious quality defects and full compensation has been received." The subject, defect scope, and compensation status in this sentence have no material support.

Configuration and implementation checks

Configuration itemHypothetical starting point for this exerciseCheck method
AI modelSelect a model that supports the required context lengthCompare the maximum context with the material length
Memory turnsAssume set to 3 roundsCompare with the prior-text range the case follow-up needs, and observe whether the agreement and report are carried in
Response limitAssume opened and set to medium lengthCompare with the character count the draft sentence needs, and observe whether truncation occurs
TemperatureAssume lowCompare with the stable requirement of customer service and knowledge-base question answering
Response formatAssume kept at defaultIf a downstream node needs a fixed structure, switch to json_schema and fill in the format requirement
Hide AI outputAssume offObserve whether the final reply is displayed directly to the user

The values above are hypothetical starting points for this exercise, not official default values.

Three common errors

  1. Treating "all goods have serious quality defects" in the draft sentence as supported by the material. The material only states that 4 of 20 sampled pieces have surface scratches, so the defect scope is not supported.
  2. Treating "full compensation has been received" as a completed fact. The material does not state a compensation status, so this expression is unsupported.
  3. Treating a retrieved similarity score as a permission or entity filter, and concluding that a high score means the record can be used in external copy.

How to verify the setup

  1. Input the hypothetical material text and the draft sentence, compare the defect scope in the draft sentence with the sampled result stated in the quality report, and judge the sentence as unsupported if it still says "all goods have serious quality defects."
  2. Input the hypothetical material text and the draft sentence, compare the compensation status in the draft sentence with the declaration status stated in the material, and record an evidence gap if the sentence still says "full compensation has been received."
  3. Input an explicit paper empty result, check the downstream node's continuation design when no quote is present, and observe whether an output that fabricates business facts appears.