What the documents look like
Dealer ordering events serve offline dealer reservations and do not directly manage platform storefronts. Source material comes from the ordering system, historical sales, dealer inventory, and financial credit approval, and it refreshes by quarter and by year. Everyday documents include the ordering handbook, the ordering contract, the sample list, and the credit approval record. Order fulfillment and after-sales question answering must stay inside these documents when a dealer follows up, and the three time-consuming steps are explaining new-product policy, collecting order quantities, and approving credit limits.
Multi-turn prompts sit on top of this material. A session identifier such as chatId decides whether the system pulls stored conversation from the database or builds context purely from the supplied messages array. The memory-round setting controls how many earlier rounds the model may reference. Retrieval similarity is a ranking value and does not act as a permission or entity filter. An interactive node returns a display configuration that carries type and params only; internal runtime fields such as entryNodeIds, memoryEdges, nodeOutputs, and nodeResponseId are not returned. Forms collect values, and business verification, approval, database writes, and external synchronization each need their own downstream implementation. Thinking strength is a reasoning budget and does not serve as a citation token limit.
A hypothetical paper exercise sample, clearly labelled and not drawn from real records:
Source text (fictional, for checking only): "2026 Spring Ordering Event Handbook, page 3: this reservation closes on 20 March 2026 and the dealer must submit the order quantity in the ordering system. Page 4: the credit limit follows the approval result of the brand channel department, and no order is confirmed before approval passes." Draft sentence: "Once the dealer submits the order quantity the order is established, and the credit limit takes effect automatically." The draft sentence treats submission as order formation and treats approval as automatic effect. The source text states only a submission action and an approval precondition, so the draft sentence is unsupported.
Constraints at the multi-turn prompts step
The prompt must state the document scope, the answering task, and how to handle missing evidence. The model must not fill in business facts that the source material does not contain. When chatId is a non-empty string, the system takes the session from the database and uses only the last item in the messages array as the user question while ignoring the remaining messages. When chatId is absent, no context is used and the supplied messages alone build the context. The memory-round value controls how many earlier rounds are referenced; a larger value more easily carries earlier dialogue forward, and it also adds content, which can raise consumption and affect response speed. A smaller value may fail to use earlier information.
For knowledge-base continuous dialogue, check whether references are resolved and whether the question is expanded. A short question does not necessarily fit that treatment. Retrieval similarity orders results and does not isolate unauthorized records or unrelated businesses. Libraries built on different vector models cannot be associated together; check the identifier first and exclude those that do not match rather than running a cross-model retrieval to compare missed recall. An interactive node returns a display configuration with type and params; a form value is a collected value and does not by itself mean the business verification or approval has passed. Thinking strength maps to a reasoning budget and cannot be moved to the citation token limit.
Configuration and implementation checks
| Configuration item | Hypothetical trial starting point | Checking method |
|---|---|---|
| AI model | A model that supports tool calling and the required context length | Compare the capabilities and points price shown in the dialog against the sample long document plus follow-up combination |
| Memory rounds | Starting point 3 rounds, because customer-service question answering usually keeps a small amount of history | Compare against 1 round and 6 rounds and observe reference resolution and consumption differences |
| Reply cap | Starting point 500, because question answering needs a short explanation | Compare against 200 and 1000 and observe whether the reply is truncated |
| Temperature | Starting point low | Compare against a higher value and observe answer stability |
| Reply format | Keep the default; choose json_schema only when a downstream step reads a fixed structure | After selecting it, fill in the matching format requirement and verify the returned structure |
| Thinking configuration | Default | Compare against "no thinking" on a simple question |
Three common errors
- Treating an absent
chatIdas a continued session. Input is a request withoutchatId; compare the context actually carried in; observe whether the context is built only from the suppliedmessages. - Treating retrieval similarity as a permission or entity filter. Input is a quote list containing
score; compare the returned records against the target dealer scope; observe whether unrelated or unauthorized records appear. - Treating an interactive node display configuration as runtime state or an approval result. Input is an
interactivereturn; compare whether it contains fields such asentryNodeIdsormemoryEdges; observe whether a form value is taken as approval passed.
How to verify the setup
- Input the sample source text and the draft sentence, compare whether the draft sentence exceeds the scope of the source material, and observe whether the answer points out that both order formation and credit effect are unsupported.
- Input two follow-up turns under the same
chatId, compare whether the second turn references the first turn, and observe whether the reference is resolved correctly; whenchatIdis not passed, observe whether only the last message is used. - Input an explicit paper empty result
[]to check the downstream handling design, compare whether the downstream treats it as an empty reference, and observe whether fabricated sources or automatic business actions appear.