What the documents look like
Ratings and experience-score maintenance works on consumer feedback that arrives after fulfillment. The main sources are Douyin Shop experience scores, Tmall service indicators, JD after-sales reviews, and customer-service system records. The daily documents are negative-review handling records, experience-score details, service diagnostic reports, and rectification ledgers. These materials update daily or weekly, and the time-consuming steps are attributing negative reviews, contacting consumers for supporting evidence, and tracking indicators after rectification. Tool calls and plugins matter here because they let a model decide, from a tool description and parameter descriptions, whether to invoke a system module or plugin, which links scattered document-checking actions together. A tool call does not change authorization status, and retrieval or form collection does not replace business approval.
Constraints at the tool calls step
A tool call needs a tool description and parameter configuration. The description states the purpose, and the parameter description together with the required flag affects whether the model issues a call. With no required parameters, the model may construct a parameter on its own. With required parameters and no suitable value in context, the model may not call the tool, and interaction can guide the user to supply the value. In advanced orchestration, available tools are connected to the tool-call module, and a small number of tools is recommended for tuning first. The tool-call termination node ends the current call, stops further tool calls, and stops the AI from summarizing tool results. Custom tool variables extend inputs so that nodes not treated as tool parameters can enter the call chain. None of these mechanisms constitutes approval of negative-review handling, rectification, or report conclusions.
Hypothetical paper exercise sample: a shop operator prepares a paper experience-score detail sheet dated 10 June 2025. The source text reads: "For this shop, 1 June to 7 June, negative reviews total 12, of which 5 have contacted the consumer for supporting evidence, 3 have evidence fully collected, 2 await consumer reply, and 2 have not been contacted." The same sheet also states: "The rectification ledger shows 4 items marked rectification complete before 7 June." The draft sentence to be judged is: "All negative reviews from 1 June to 7 June have completed rectification and been closed." The request turns a partial status into a completed whole and is a handover request that requires checking.
Configuration and implementation checks
| Configuration item | Hypothetical starting value for this exercise | Object of comparison and comparison method |
|---|---|---|
| Tool description | States "used to summarize negative-review status in the experience-score detail sheet" | Compare with the field scope of the paper material and confirm the description does not widen the object |
| Required parameter | Set to "statistics start and end dates" | Check whether the context contains those dates; when absent, record whether the model did not call |
| Tool connection | Only the experience-score detail summary tool is connected | Compare against the advanced-orchestration connection points and confirm no unrelated module is attached |
| Tool-call termination | Placed after the summary tool | Observe whether AI summary output still appears after termination |
| Custom tool variable | Adds a "negative-review status" description | Compare item by item with the material fields and confirm status values can be referenced |
| Retrieval scope | Limited to this shop's 1 June to 7 June materials | Check whether returned content includes records outside that period, and record the actual observation |
The hypothetical starting value "statistics start and end dates" is chosen as the required parameter because the material itself gives a weekly range; the comparison method is to look for that date field in the context. These values and fields are paper assumptions, not official defaults.
Three common errors
- Treating "contacted the consumer for supporting evidence" as "evidence fully collected." The input is the source text above; compare the two status fields "contacted" and "fully collected." If the draft sentence writes "evidence complete," that is status conflation.
- Extending "4 items marked rectification complete" to "all negative reviews have completed rectification." The input is the rectification ledger text; compare 4 items with the total of 12 negative reviews. If the draft sentence writes "all complete," that is scope expansion.
- Treating a successful tool call as business approval. The input is the summary result returned by the tool call; compare that result with the authorization field. If negative-review handling is deemed approved on that basis, that is treating a run result as authorization.
How to verify the setup
- Input the source text above and the draft sentence; compare the fields "fully collected" and "contacted," and observe whether the draft sentence writes uncollected evidence as collected. The expectation is that the status difference is identified; not actually tested.
- Input the rectification ledger text; compare the quantities "4 items" and "12 items," and observe whether the draft sentence writes a part as a whole. The expectation is that the scope difference is identified; not actually tested.
- Input the tool-call return result and the authorization field; compare whether the two are the same fact, and observe whether wording appears that treats a call result as approval. The expectation is that the authorization gap is identified; not actually tested.
If retrieval returns an empty result, an explicit paper input "[]" can be used to check the downstream design, and this cannot be used to conclude that a rule retrieval is necessarily empty. Libraries across different vector models cannot be associated together; first check the identifier and exclude non-matching ones, then record the actual observation.