Fresh-food supplier claims: multi-turn prompts for staff training

Review multi-turn prompts for staff training in Fresh-food supplier claims. Check sources, input boundaries, error handling and repeatable test evidence.

What the documents look like

Fresh-food supplier claims pursue responsibility for quality at arrival. They do not handle natural in-store shrinkage, which is a separate concern from near-expiry markdown. The working materials are quality exception forms, photo evidence, deduction notices and claim reconciliation statements. Quality inspection systems record batch determinations, WMS receiving records capture received quantity and time, supplier contracts define the boundary of responsibility, and financial reconciliation supplies the basis for deductions and compensation. Updates follow each batch and each month. Store service and internal training involve explaining claim positions to stores, helping to fix evidence, and guiding negotiation over replacement goods or payment. A multi-turn conversation must keep batch, supplier, exception type and quantity range consistent across turns, and the prompt must state the document scope and how to behave when evidence is missing.

Constraints at the multi-turn prompts step

Store staff often describe a symptom first, then add the batch and photos, then ask about the deduction position. Multi-turn dialogue must ensure that earlier and later turns refer to the same quality exception form, the same supplier and the same period. Context carried in from history needs actual checking; a new session and a continued session draw input from different sources. Question optimization is used to check pronoun resolution during continuous knowledge-base conversation, and whether it applies to short questions depends on the actual configuration. Model context length and reply cap need to be tested together with the long-document-plus-follow-up combination. Reply format is chosen according to what comes next; selecting json_schema requires filling in the corresponding format requirement and verifying the returned structure.

Hypothetical drill sample: a store submits a quality exception form whose text reads "Batch A arrived on 10 March 2025, supplier Jia, spinach leaves extensively wilted, 12 boxes rejected at receiving, three photos attached." The draft sentence to be judged is "This batch has been confirmed as the supplier's responsibility, and the deduction notice has been issued." Judging that draft sentence requires comparing whether the quality exception form has entered a quality inspection determination, whether the deduction notice is in an issued state, and whether supplier responsibility already has a contract or inspection basis. The sample shows only the store submission and photos; no inspection determination conclusion and no record of the deduction notice being issued appear, so the draft sentence is unsupported.

Configuration and implementation checks

The table below lists settings or check items relevant to this feature and the primary task, built around the hypothetical sample. The values are starting points for this hypothetical trial, not official defaults.

Configuration itemStarting point for this hypothetical trialReason and comparison method
Memory turns4 turnsCovers the four turns of store description, batch supplement, photo upload and deduction follow-up; compare whether fewer turns lose batch information
Reply capOn, set to medium lengthMust hold the position explanation and evidence list; compare whether truncation falls on a key quantity field
TemperatureLowClaim positions need stability; compare whether the same batch yields a consistent answer at different temperatures
Reply formatDefaultNo downstream need to read a fixed structure; compare whether json_schema introduces extra field requirements
Multimodal recognitionImage enabled according to model capabilityPhoto evidence needs the model to read it; compare whether, when disabled, the answer relies only on the text description
Question optimizationChecked against continuous knowledge-base conversationCheck whether pronoun resolution correctly links "this batch" to Batch A

Three common errors

  1. Treating a store-submitted quality exception form as a completed quality inspection determination. The input is the store submission record, the comparison object is the quality inspection system determination conclusion, and the observable result is that no responsibility confirmation may be output while the inspection conclusion is missing.
  2. Treating a deduction notice draft as issued. The input is the notice draft, the comparison object is the issued-status field, and the observable result is that the conversation may not state it has been issued while the status remains unupdated.
  3. Treating a retrieval similarity score as a basis for permission or supplier isolation. The input is the retrieval return list, the comparison object is the supplier identifier and store authorization scope, and the observable result is that a high or low score does not change the visibility of unauthorized records.

How to verify the setup

  1. Input the hypothetical sample text and the draft sentence "This batch has been confirmed as the supplier's responsibility, and the deduction notice has been issued." Compare the quality inspection determination and the deduction notice issued status, and observe whether the answer points out that both the responsibility confirmation and the notice issuance are unsupported.
  2. Input four consecutive turns under the same chatId. Compare whether, with memory turns set to 4, Batch A and supplier Jia are retained, and observe whether batch information is lost at a lower turn count.
  3. Input an empty quote list []. Compare whether the downstream design handles the empty result as such, and observe whether content is fabricated as a retrieved rule. The retrieval check records only actual observations and does not guarantee a first hit or full recall.