What the documents look like
Payment risk work in this category centres on card network disputes and receiving accounts, which places it in a different document scope from parcel status tracking. The main material comes from Stripe or PayPal back office records, Shopify orders, carrier proof of delivery, and customer service email. Day-to-day documents include payment chargeback notices, fraudulent order lists, invoices, and logistics proof of delivery. The brief marks data update frequency as daily and per order; this page does not infer batch synchronisation, full synchronisation, real-time updates, or transaction-triggered mechanisms from that label.
For multi-turn dialogue in store service and internal training, chargeback notices, order records, and proof of delivery need to be brought in across separate turns, and each turn must state which source range its answer draws on. A prompt should set out the material scope, the answering task, and the handling method when evidence is missing, so the model does not write in missing business facts. Retrieval and similarity scores do not perform permission or entity isolation duties; retrieval is not authorisation. Forms collect values only; business verification, approval, database writes, and external synchronisation each require their own stated downstream implementation.
Hypothetical drill sample: a store service role at an independent site plans to submit a handover request to an internal training assistant. The material text supplied for checking is three sentences. Sentence one is a chargeback notice extract: "Order A-1042 received a chargeback notice on 8 March 2025, with status pending review." Sentence two is an order record extract: "Order A-1042 shipped on 2 March 2025, carrier proof of delivery time 6 March 2025." Sentence three is an invoice extract: "Invoice INV-2087 corresponds to order A-1042; amount and currency follow the invoice original." The draft sentence to be assessed is: "Order A-1042's chargeback has been fully resolved, proof of delivery has been submitted, and the fund freeze has been lifted." That draft sentence carries a subject, an object, and three status statements, while the source text shows only that the chargeback status is pending review, and records nothing about resolution, evidence submission, or fund release.
Constraints at the multi-turn prompts step
Multi-turn dialogue relies on four checks: history records, question optimisation, model length, and reply format. History records require confirming which context is actually carried in, distinguishing input for a new session from input for a continued session. Question optimisation applies when checking reference resolution and question expansion for continuous knowledge-base dialogue; not all short questions are suited to it. Model and length require checking context capacity against reply limits, and testing long-document plus follow-up combinations. Reply format follows what the downstream step will consume; when json_schema is selected, the corresponding format requirement must be filled in and the returned structure verified.
The hypothetical sample fixes the material scope for this page. The three source sentences name one order, one chargeback notice, one invoice, and one carrier delivery time. The draft sentence adds three status claims that the source text does not carry. Each turn of the training dialogue should restate which sentence it is drawing on, so a reviewer can compare the draft claim against the same field in the source.
Configuration and implementation checks
Configuration below addresses the multi-turn checking needs of the hypothetical sample. Values are a hypothetical trial starting point for this page only, with the comparison object named; they are not official defaults.
| Configuration item | Hypothetical trial starting point | Reason and comparison method |
|---|---|---|
| Memory turns | Keep a small number of history turns | Compare against the sample's three material turns plus one follow-up, to confirm whether earlier turns are carried in |
| Reply limit | Set according to the sample explanation length | Compare against the character count the draft sentence needs, to check for early truncation |
| Temperature | Lower | Compare against a rule-clear checking scenario, to observe whether answers stay stable |
| Reply format | Keep default | Compare against whether a downstream node reads a fixed structure; if needed, switch to json_schema and fill in the format requirement |
| Multimodal recognition | Enable by the actual input type | Compare against the capability shown for the model; unsupported types are not sent to the model |
| Hide AI output | Choose by whether a downstream node continues processing | Compare against whether a downstream node continues processing; when enabled, content can still pass through reply output |
The thinking configuration aligns with reasoning_effort in the OpenAI specification and is adapted by ai-proxy for different model platforms. Thinking budget is a reasoning budget and is not a citation token limit. Some models do not fully support all thinking options; if an error appears after switching, the default option can be restored.
Three common errors
- Treating pending review as fully resolved. The sample source text states only that the chargeback status is pending review, while the draft sentence writes that the chargeback has been fully resolved. Input is the source sentence and the draft sentence; the compared field is chargeback status; the condition is met only if the source text shows a same-field value of fully resolved. At present this is unsupported.
- Treating a delivery time as proof of delivery submitted. The source text records only the carrier proof of delivery time, and does not record that proof has been submitted. Input is sentence two and the draft sentence; the compared object is submission status; the source text has no such field, so this is an evidence gap.
- Treating a retrieval result or a form entry as authorised execution. Retrieval is not authorisation, and a form collects values only. Input is one retrieval or one form submission; the compared object is whether an authorisation record exists; when absent, record the gap and do not infer that the fund freeze has been lifted.
How to verify the setup
- Input the sample's three source sentences and the draft sentence; compare the three claims in the draft sentence — resolution, evidence submission, and fund release — against the same-field values in the source text; observe whether a same-field contradiction or an unsupported claim appears. Expected result: all three claims are unsupported.
- Input an empty-result continuation scenario, using the explicit paper input [] to check the downstream node design; compare whether the downstream handles it as an empty reference; observe whether missing business facts are written in. Do not claim that a fictional rule retrieval necessarily returns empty.
- Input the sample follow-up; compare the history turns actually carried in against the memory-turns setting; observe whether earlier turns are brought in. Libraries across different vector models cannot be associated together; first check the identifier and exclude non-matching ones, record the actual observation, and do not run a cross-model retrieval to compare missed recall.
These checks use the hypothetical source text, draft sentence, and request given above. They state what to input, what to compare, and what phenomenon would satisfy the condition. They do not claim that any test has been run, that any deployment or approval has occurred, or that any business action has been completed. Missing evidence remains missing.