What the documents look like
Rebate calculation for dealers happens after sales close. The core work is reconciling policy definitions against financial treatment, and the working material comes from ERP sales records, dealer remittances, contract ledgers and the finance system. The documents handled day to day are rebate policies, volume achievement tables, reconciliation statements and red-letter invoices. In store service and internal training, a multi-turn conversation has to stay anchored to these documents. Achievement definitions, return amounts and invoice offsetting that appeared in earlier turns directly shape what a later answer may cite. The material updates monthly or quarterly, and this page's feature does not depend on real-time synchronization.
Three industry facts shape the task. Rebate calculation sits after the sale, so the conversation concerns policy interpretation and financial posting rather than order taking. The data arrives from several systems at once, which means a prompt has to state which document set is in scope for the current answer. Errors surface as rebate disputes, channel conflict and incorrect revenue recognition, so the prompt must refuse to supply a business fact that the supplied material does not contain.
Constraints at the multi-turn prompts step
The prompt needs to state the material scope, the answering task and the handling of missing evidence, so the model does not write business facts on its own. For history, the actual context carried into the request has to be checked, and a new session versus a continued session differ in what input they accept. For question optimization, a knowledge-base continuous conversation needs checking of pronoun resolution and question expansion; a short question does not automatically qualify. For model and length, the supported context and reply settings have to be checked, and long-document plus follow-up combinations tested. Reply format follows whatever consumes the output; choosing json_schema requires filling in the corresponding format requirement and verifying the returned structure.
Hypothetical drill sample, prepared for internal training and clearly labelled as a paper exercise, not a real record: the source material reads, "The March 2025 reconciliation statement shows dealer A remitted 1.2 million, volume achievement rate 92%, return amount 80,000. The rebate policy states that an achievement rate above 90% earns a 3% rebate, and the return amount is deducted from the rebate base." One draft sentence awaits judgment: "Dealer A's rebate amount this month is 36,000." That draft sentence carries a subject, a period and an amount, and has to be checked against the source wording for the calculation basis and the deduction item.
Configuration and implementation checks
| Configuration item | Hypothetical trial starting value | Reason and comparison method |
|---|---|---|
| AI model | A model documented as supporting tool calling and long context | Rebate reconciliation spans several documents, so the model must handle longer input; comparing against a short-context model shows truncation behavior |
| Memory turns | Hypothetically set to 4 turns | Training needs the policy basis and reconciliation figures from earlier turns; comparing against 1 turn shows whether the earlier deduction item is lost |
| Reply cap | Hypothetically opened at a medium length | A rebate explanation needs a complete statement; comparing against the cap left unset shows whether truncation appears |
| Temperature | Hypothetically set low | Rebate calculation is a rule-defined scenario needing stable answers; comparing against a higher setting shows whether the basis drifts |
| Reply format | Hypothetically left at default | Training dialogue does not need a fixed structure; comparing against json_schema shows whether a downstream step can read the output directly |
| Hide AI output | Hypothetically left off | Training needs the intermediate reconciliation shown; comparing against it being on shows whether the user sees only the final answer |
These are implementation checks and hypothetical starting values, not documented defaults. Each value needs its reason stated and a comparison method, and none should be read as an official setting.
Three common errors
- Treating a retrieval hit as an authorization basis. A knowledge-base search returning a reconciliation statement fragment does not mean the dealer has received rebate approval. The check must separate the retrieval hit from the business approval state, because retrieval does not perform permission filtering.
- Treating an interaction node's display configuration as an execution result. An interaction node returning a
userSelectoruserInputdisplay configuration contains onlytypeandparams; after submission the workflow still needs another conversation request to continue. A pending review state does not show that approval passed. - Treating a similarity score as an entity filter. The similarity score is a retrieval ranking reference and does not carry dealer identity or permission isolation. The check must confirm data ownership and access scope separately.
How to verify the setup
- Input the reconciliation source text and the draft sentence from the hypothetical sample, then compare the rebate amount in the draft against the calculation basis in the source. The source states achievement rate 92%, return amount 80,000 and rebate rate 3%, while the draft states 36,000. Observe whether the model points out that the return amount must be deducted from the rebate base, and whether it flags that the rebate base definition needs checking.
- Input a follow-up that lacks the rebate policy source text, for example "How much is dealer A's rebate this month?" Compare whether the answer cites a policy clause that was not provided. Observe whether the model states clearly that the policy source text is missing and that a definite amount cannot be given.
- Input a session request that contains an interaction node, then compare the returned
interactivefield content. Observe whether the returned content contains onlytypeandparams, and whether internal runtime fields such asentryNodeIdsare absent. If internal fields appear, record that as an item to verify.
Each check states the input, the comparison and the observable result. None of these checks has been executed, and no test, deployment, approval or refund is claimed to have happened. Where a document field is not supplied, that field remains unknown.