What the documents look like
Warehouse picking, checking and packing handles outbound orders rather than supplier arrivals. The working material comes from the WMS, the order system, the electronic waybill platform and weighing equipment. Day-to-day documents include wave picking lists, checking records, waybills and packing consumable sheets. Updates arrive per order and per hour, so the context carried across turns changes as an order advances: a wave number, a checking status or a waybill detail mentioned in one turn may already differ in the next.
For store service and internal training, a multi-turn prompt has to name the document scope, the answering task and the treatment of missing evidence, and it must not let the model supply business facts that the source does not contain. The documents themselves decide what a draft sentence may claim. A wave picking list records which orders belong to a wave and their picking status. A checking record records scan-check results. A packing consumable sheet records which materials correspond to a wave. A waybill records whether printing has occurred. None of these fields authorizes packing, dispatch or payment by itself.
The following hypothetical drill sample is used throughout this page. It is a paper exercise, not a record from any real warehouse.
Hypothetical drill sample: the training material reads, "On 12 May 2024, wave W-0512-03 contains 12 orders. The checker has completed scan checking for 8 of these orders. The remaining 4 orders are awaiting checking. Packing consumable sheet P-0512-07 corresponds to this wave. The waybill has not been printed." The draft sentence under review reads, "Checking for wave W-0512-03 is fully complete and packing may begin."
Constraints at the multi-turn prompts step
Multi-turn dialogue depends on history. The actual context carried into a turn has to be checked, and a new session has to be distinguished from a continued session. When chatId is a non-empty string, FastGPT takes the session from its database and uses the last element of the messages array as the user question, ignoring the remaining messages. Training therefore has to confirm which question text was actually submitted in each turn. When chatId is not passed, the platform does not use its own context function, and the context is built entirely from the submitted messages.
Question optimization for knowledge-base dialogue requires checking reference resolution and question expansion. Short questions do not all behave the same way, and continuous dialogue is a precondition rather than a default. Model and length settings require checking the supported context and reply configuration, then testing long documents combined with follow-up questions. Reply format follows what the downstream step consumes; when json_schema is selected, the corresponding format requirement has to be filled in and the returned structure verified.
Retrieval and similarity scores do not perform permission or entity isolation. A retrieval hit is not authorization, and a relevance score is not a category, entity or permission filter. A similarity setting does not exclude unrelated businesses or unauthorized records. Interaction nodes collect values; business verification, approval, database writes and external synchronization need their stated downstream implementation. A form value is not a verification result, and an interactive waiting state is not an approval.
For the drill sample, the draft sentence and the source disagree on the same subject, the same period and the same field. The source states that 4 orders remain awaiting checking; the draft states that checking is fully complete. These are mutually exclusive values for the order status field, so the draft sentence is not supported.
Configuration and implementation checks
| Configuration item | Hypothetical starting point for this drill | Check method and reason |
|---|---|---|
| AI model | Select a model whose displayed maximum context covers the sample text | Compare the maximum context and tool-calling information shown under the model against the combined length of the wave, order and consumable text |
| Memory turns | Keep the default value initially | Check how many history turns are actually carried into a continued session, and compare with the follow-up range the training requires |
| Reply limit | No fixed value | Observe whether a reply is truncated, and compare with the complete explanation the sample requires |
| Temperature | Lower setting | Check whether repeated generation of the same draft sentence stays stable, and compare with a rule-bound checking scenario |
| Reply format | Default | Switch to json_schema with a filled format requirement only when a downstream node needs a fixed structure |
| Multimodal recognition | Decided by model capability | Enable only when the configuration dialog shows support; unsupported types are not sent to the model |
These are implementation checks for this page, not documented defaults. Each starting value is a hypothetical choice made for the drill, with the comparison method stated so the result can be observed.
Three common errors
- Treating an awaiting-checking status as complete. In the sample, the source states that 4 orders remain awaiting checking, while the draft says checking is fully complete. This is a mutually exclusive value on the same field, so the draft is not supported. Executable check: input the source text and the draft sentence, compare the order status field, and mark the draft as failing when the source says awaiting checking and the draft says complete.
- Treating a retrieval result as an authorization basis. A similarity score is not a permission or entity filter, and a retrieval hit does not mean an operation has been approved. Executable check: input one retrieval hit record and one handover request, compare whether the request states an authorization status, and record the item as pending verification when only the retrieval result exists and no authorization statement is present.
- Treating an interaction node return as completed execution. A form collecting values is not business verification or a database write, and an interactive waiting state is not an approval. Executable check: input the
interactiveconfiguration returned by the interaction node and the subsequent request, compare whether an explicit approval or write result exists, and record the item as not executed when only form values are present.
How to verify the setup
- For the sample source and the draft sentence, input both texts and compare the order status field. If the source says awaiting checking and the draft says complete, the draft fails; if the source and draft agree on status, the check passes. This verifies the primary task of drafting against supplied source wording.
- For a continued session, input two turns of messages under the same
chatId, compare the history preview actually carried in against the submitted question text, and confirm that the last turn content is used as the user question while the remaining messages are ignored. - For empty-result handling, input an explicit paper retrieval result of
[]and compare whether the downstream design handles an empty list. Do not claim that a fictional rule retrieval necessarily returns an empty result. If the downstream branch for empty results is undefined, record it as a gap.
Uncertain background stays conditional. Where a document field is not provided, the value is unknown. Missing evidence remains missing, and a pending request is not an approval.