What the documents look like
Fruit-store procurement runs on purchase orders, acceptance sheets, pesticide-residue test reports and shrinkage records. The material changes by batch: the same supplier can present a different quote, grade and test conclusion on the next delivery. Wholesale-market price signals and grade-based acceptance drive the work, and no head-office standard meal plan exists to serve as a fixed reference. Inquiry and price comparison, grade judgment at acceptance and shrinkage control are the time-heavy steps, so follow-up questions in a conversation usually attach to one batch's concrete state.
That batch-level change shapes the multi-turn prompts step. A prompt has to state the material scope, the answering task and how to behave when evidence is missing, so the model does not fill in business facts that were never supplied. Historical turns, question optimization, model context length, reply settings and reply format all need checking against the actual batch under discussion.
Constraints at the multi-turn prompts step
The conversation must separate what the documents state from what a draft sentence claims. When the acceptance sheet records a downgrade and a draft says the whole batch passed at the original grade, the two do not agree on the grade conclusion, and the draft stays unverified.
Historical context differs between a new session and a continued session, so the turns actually sent in the request need checking rather than assuming. Question optimization applies to continuous knowledge-base conversation; coreference removal and question expansion are checked there and are not assumed for every short question. Model and length settings are checked against the combined length of purchase order, acceptance sheet and test report, and long-document plus follow-up combinations are tested. Reply format follows downstream needs; selecting json_schema requires filling in the corresponding format requirement and checking the returned structure.
Hypothetical drill sample: the source text reads "On 10 March 2025 a store issued a purchase order to stall A for Red Fuji apples, 200 boxes, agreed grade one, unit price settled against that day's wholesale-market price. The 11 March 2025 acceptance sheet records: 200 boxes arrived, of which 12 boxes fell below the grade-one fruit-diameter standard, acceptance conclusion downgraded acceptance, the downgraded portion priced at grade two. The pesticide-residue test report for the same batch carries the hypothetical number JC-20250311-01 and a conclusion of pass." The draft sentence to be judged reads "This batch of Red Fuji apples was accepted entirely at grade one, with no downgrade." That sentence disagrees with the acceptance sheet on the grade conclusion and is an item awaiting verification.
Configuration and implementation checks
The settings below are starting points for this hypothetical drill, used for comparison against the sample text. They are not official default values.
| Configuration item | Hypothetical starting point | Reason and comparison method |
|---|---|---|
| AI model | A model whose displayed maximum context covers the combined material | Compare the model information's maximum context against the combined length of purchase order, acceptance sheet and test report |
| Memory turns | A small number of turns | Compare against the preceding turns needed for follow-up on the same batch, confirming the turn holding the acceptance conclusion is carried in |
| Reply limit | Enabled at a medium length | Compare against the length a complete statement of the grade conclusion needs, confirming that conclusion is not truncated |
| Temperature | Set low | Compare against the stability expected for customer-service and knowledge-base question answering, confirming the grade conclusion is stated consistently |
| Reply format | Left at default | Compare against downstream needs; if downstream reads a fixed structure, switch to json_schema and supply the format requirement |
| Multimodal recognition | Image recognition enabled according to model capability | Compare against whether acceptance sheets and test reports arrive as images; unsupported types are not sent to the model |
Three common errors
- Treating "accepted entirely at grade one" in the draft sentence as confirmed fact. The sample acceptance sheet records 12 boxes as downgraded acceptance, the draft disagrees with that record on the grade conclusion, and the item remains awaiting verification rather than usable.
- Treating a retrieval result as authorization. Purchase-order or acceptance-sheet content returned from the knowledge base shows the material exists; it does not show that the batch has payment or settlement authorization, and authorization status requires a separate downstream system implementation.
- Treating a form returned by an interactive node as already submitted. The inputForm returned by an interactive node holds display configuration only; collected form values do not mean business verification, approval or a database write has completed.
How to verify the setup
- Input the sample source text and the draft sentence "This batch of Red Fuji apples was accepted entirely at grade one, with no downgrade," then compare the draft against the acceptance sheet record of 12 boxes downgraded acceptance. The condition is met when the reply marks the draft as inconsistent with the record or awaiting verification; it is not met when the reply confirms grade one throughout.
- Input a continued-session request under the same chatId, then compare the historical turns actually carried in against the memory-turns setting. The condition is met when the carried turns include the turn holding the acceptance conclusion; when that turn is absent from the history, record the gap.
- Input a paper material containing only a purchase order and no acceptance sheet, then compare whether the reply states a conclusion on the acceptance grade. The condition is met when the reply states that acceptance evidence is missing and gives no grade conclusion; it is not met when the reply gives a grade conclusion. Empty-result handling can be checked against a clearly defined paper input, and this does not assert that a search for a fictional rule must return an empty result.