What the documents look like
Store count and loss-control records center on book-to-physical variance and shrinkage. They do not handle supplier settlement. The main sources are POS inventory, WMS, monitoring systems, and the SB/T count specification. Everyday documents include count sheets, variance analyses, loss-report approvals, and monitoring review records. Typical roles are the store loss-prevention department, store inventory keeper, and financial auditor. Update frequency is monthly or quarterly. Errors produce distorted inventory, inflated gross margin, and employee fraud. These facts set the scope of this page: only multi-turn dialogue and prompts inside the count and loss-control setting, with training material traceable to fields in count sheets, variance analyses, loss-report approvals, or monitoring review records.
Constraints at the multi-turn prompts step
Four constraint areas apply here. Prompts must state the material scope, the answering task, and how to behave when evidence is missing, so the model does not write in business facts. History must be checked against the context actually carried in, separating a new session from a continued session. Question optimization, when a knowledge base needs continuous dialogue, must be checked for reference resolution and question expansion; not every short question fits. Model and length must be checked against the context and reply settings the model supports, testing long documents combined with follow-up questions. Reply format follows downstream consumption; when json_schema is selected, the corresponding format requirement must be filled in and the returned structure checked. Retrieval is an information-acquisition process and does not perform authorization judgment; a similarity score is not a permission or entity filter.
Hypothetical drill sample: three sentences from internal training material of a supermarket loss-prevention department. Sentence one: in the store-closing count of 31 March 2025, store A's SKU-4471 had a book inventory of 120 units and a physical count of 108 units. Sentence two: the variance analysis sheet classifies the difference as pending verification, and loss-report approval is not yet complete. Sentence three: the monitoring review record shows a review request for the corresponding shelf during that period, and the review result is not stated. Draft sentence to be judged: the 12-unit difference for SKU-4471 has been confirmed as shrinkage by loss-report approval. Judging compares the draft sentence with the source: the source states only the count quantities and the pending-verification status, loss-report approval is incomplete, and the monitoring review result is not stated; the draft rewrites pending verification as confirmed, which is unsupported.
Configuration and implementation checks
Configuration below stays tied to the sample and to settings the official documents describe, or to implementation checks. Values are hypothetical starting points for this drill, not official defaults. The documents state that memory rounds, reply cap, and temperature are configurable and give no fixed numbers.
| Configuration item | Hypothetical drill starting point | Reason and comparison method |
|---|---|---|
| Memory rounds | Hypothetical start of 3 rounds | Training dialogue needs earlier variance context; compare against a smaller count for loss of SKU and period |
| Reply cap | Hypothetical start of 800 | Variance explanation needs completeness; compare against a shorter cap for truncation of key fields |
| Temperature | Hypothetical start low | Rule-bound scenarios need stability; compare against a higher value for answer drift |
| Reply format | Decided by downstream consumption | When fixed fields must be read, select json_schema, fill the format requirement, and check the returned structure |
| Multimodal recognition | Checked against model capability | Image recognition serves screenshots or table images; when the model does not support it, the option is not shown |
| Thinking configuration | Shown according to model support | Complex variance tracing may use a higher strength; compare against the default to observe answer differences |
Three common errors
- Rewriting pending verification as confirmed. Input is the sample source and the draft sentence; compare the loss-report approval status field; the observable result is that the draft claims approval confirmation while the source states only pending verification. This treats an incomplete state as complete.
- Treating retrieval results as authorization. Input is one dialogue containing a count-variance query; compare the returned content against operating permission; the observable result is that retrieval returned records, but the records do not constitute loss-report approval or data-write permission. Retrieval does not perform authorization.
- Jointly associating across vector model libraries. Input is two retrieval library identifiers from different vector models; compare the library identifier and the model identifier; the observable result is that the identifiers do not match, and the mismatched one must be excluded before recording actual observations. Do not run a cross-model retrieval to compare missed recall.
How to verify the setup
- Prompt scope check. Input is the three source sentences and the draft sentence; compare whether the draft writes in a loss conclusion the source does not state; the observable result is that the draft shows confirmed shrinkage while the source states only pending verification, so the draft is judged unsupported.
- History carry-in check. Input is a continued-session request under the same chatId and its message array; compare the context rounds actually carried in against a new-session input; the observable result is that the continued session carries earlier text while the new session does not; when chatId is empty, context is built entirely from messages.
- Empty-result downstream check. Input is a paper input of an empty quote list; compare whether the downstream design treats the empty result as no material; the observable result is that the downstream outputs along the no-material path, without claiming that a fictional rule retrieval necessarily returns empty. Missing evidence stays missing; a pending review does not prove the absence of operating permission, and a review request is not a review result.
Separately, a retrieval check records observation and failure branches rather than guaranteeing a first hit, full recall, or that an unrelated query returns empty. Cross-model libraries cannot be jointly associated; verify identifiers first, exclude non-matching ones, and record actual observations. The optional paths in the overall flow diagram do not mean every request executes all of them: image retrieval requires a corresponding image input and model capability, and reranking requires a model and must be enabled by setting. Node behavior already present, actually configurable fields, manual verification actions, and business actions that need a separate system implementation are distinct. RRF is a hybrid-result fusion process and is not written as an on/off switch. Interactive waiting, record status, and hidden dialogue are explained as the documents describe them, without inventing corresponding control switches, submission counts, automatic judgments, or snapshot guarantees. A thinking budget is a reasoning budget and must not be reused as a quote-token cap.