What the documents look like
Franchisee onboarding transfers operating capability rather than filing a legal disclosure, so the material set differs from franchise registration work. The records come from a training system, a supervisor app, store photographs and a headquarters SOP library. Everyday documents include training sign-in sheets, opening checklists, SOP manuals and assessment scores. Copy drafting for goods and marketing draws on these materials, and any promotional sentence must trace back to a specific record. Content that the record does not state stays pending verification. In multi-turn use, sign-in, checklist and assessment details carried in from earlier turns affect later drafts, so the actual number of history turns and the material scope need checking against what the conversation really brings in.
Constraints at the multi-turn prompts step
The prompt needs to state the material scope, the drafting task and the handling of missing evidence, so the model does not write business facts on its own. When a session continues, the system retrieves the conversation from the database by chatId and uses only the last item of the messages array as the user question; the remaining messages are ignored. A new session that passes no chatId builds context entirely from messages. Where a knowledge base needs continuous dialogue, coreference resolution and question expansion are checked, and short questions do not all qualify. The model's maximum context and reply cap must be checked together with long documents and follow-up combinations. Reply format follows downstream needs; selecting json_schema requires filling in the corresponding format requirement and verifying the returned structure.
Hypothetical paper exercise sample: the source text reads, "March 2025, Cashier Role SOP Manual, page 4: before a new store opens, basic cash register operation training must be completed, and the sign-in sheet is confirmed by the store manager." A separate Opening Checklist lists "cash register operation training" as a pre-opening item. The draft sentence under review is: "All franchise stores of this brand have passed cash register operation training certification." The subject scope of this sentence exceeds the single store and single period described in the material, and the material does not state a "certification" status, so the sentence is unsupported.
Configuration and implementation checks
| Configuration item | Value or check action on this page | Basis and explanation |
|---|---|---|
| AI model | Select a model supporting the required context length | Maximum context determines how much content can be referenced |
| Memory turns | Hypothetical trial starting point of 3 turns, compared with 1 turn and 6 turns to see whether carried material is sufficient | This is a hypothetical starting point, not an official default |
| Reply cap | Set according to the length needs of the draft sentence, compared with truncation behaviour | Too low causes early truncation |
| Temperature | Keep low, compared with stable restatement of material | Copy drafting may be raised somewhat |
| Reply format | Keep default for ordinary drafting; select json_schema and fill the format requirement when a fixed structure is needed | Changed only when a downstream flow reads a fixed format |
| Multimodal recognition | Enable only when the model supports it and store photographs must be read | Depends on model capability; unsupported types are not sent |
Three common errors
- Writing a single-store, single-period training record as if all franchise stores had been certified. For the check, input the source text above together with the draft sentence, compare the subject scope and the status field, and judge the sentence unsupported because the material does not state "certification".
- Assuming that all messages are carried in when a session continues. For the check, input multiple messages with a chatId, compare the last item actually used against the remaining ignored items, and confirm the carried scope.
- Treating retrieval relevance as a permission or entity filter. For the check, input the same query, compare the returned records against the target authorized scope, and note that the relevance score does not perform exclusion of unrelated business.
How to verify the setup
- Input the hypothetical source text and the draft sentence, then compare whether the subject, object and status of the draft sentence fall within the material scope. Any excess means the condition is not met.
- Input a continued-session request carrying a chatId, then compare the user question actually used by the system against the expected last item. Agreement means the condition is met.
- Input an explicit paper-level empty result [], then check whether the downstream design handles the empty result. Do not claim that a fabricated rule retrieval necessarily returns empty.