What the documents look like
In fresh-food weighing and sorting, fulfillment happens inside the warehouse and the operative facts are weight and specification. The working materials are picking task sheets, weighing labels, short-weight compensation forms, and quality inspection records. These come from the WMS, electronic scales, the order system, and inspection photos. They refresh per order and per day. For copy drafting, a sentence may only rely on the weight, specification, status, and validity scope that a material actually states. Anything a material leaves unstated stays a gap, and the draft keeps that gap open rather than filling it.
This matters because a marketing sentence about a weighed product is a factual claim about a specific batch. A picking task sheet can carry a specification tolerance, a weighing label can carry a printed weight, and an inspection record can carry a condition statement. Each of these supports a different part of a sentence. A draft that merges them into one uniform claim goes beyond what the materials say.
Constraints at the multi-turn prompts step
Multi-turn drafting requires checking which context is actually carried in. When a session continues with chatId, the interface takes the last item of the messages array as the user question and ignores the remaining messages. When chatId is not passed, the FastGPT-provided context feature is not used, and context is built entirely from the messages that are passed. A prompt must therefore state the material scope, the answering task, and how to handle missing evidence, so the model does not write business facts on its own. Reply format follows what the downstream step needs; choosing json_schema requires filling in the corresponding format requirements and checking the returned structure.
Hypothetical paper exercise sample. The material text reads: "Picking task sheet A-0417 states: item is loose spinach, specification 500g±20g, picking batch is the April 17 morning shift; the quality inspection record shows leaves intact with no yellow spots. The weighing label printed weight is 512g." The draft sentence under review reads: "Every portion of this spinach is exactly 500g, with leaves intact and no yellow spots." Checking this sentence, the material supports only the two values "specification 500g±20g" and "512g," and does not support "every portion is exactly 500g." The phrase "leaves intact with no yellow spots" has a corresponding statement in the inspection record and is a supported part.
Configuration and implementation checks
| Configuration item | Hypothetical trial starting point for this page | Reason for the choice and comparison method |
|---|---|---|
| AI model | Choose a model in the dialog that supports multimodal recognition | Inspection photos need to be read as image input; compare available configuration items against a model without multimodal support |
| Memory rounds | Keep a small number of history rounds | Customer service and knowledge-base Q&A applications usually keep a small number of history rounds; compare context completeness against keeping no history |
| Temperature | Low | Copy checking requires stable answers; compare judgment consistency on the same material against a higher value |
| Reply format | Default | Ordinary Q&A keeps the default; compare against json_schema when a fixed structure is needed |
| Multimodal recognition | Turn on image recognition | Inspection photos are image material; compare whether image content can be read against leaving it off |
| Hide AI output | Off | Debugging observes intermediate model results; compare visible output against turning it on |
These are hypothetical trial starting points, not official defaults. Each is chosen for the specific material in the sample and is compared against a named alternative so the effect can be observed rather than assumed.
Three common errors
- Writing an unstated value as a fixed value. In the sample, the material states only "500g±20g" and "512g," while the draft sentence writes "exactly 500g." This turns a range into a fixed value. The check is to compare each number in the draft against the numbers in the material word by word; a fixed value with no corresponding statement in the material is recorded as unsupported.
- Treating a pending review as an approval. If a short-weight compensation form is in a pending-review state, a draft sentence that writes "compensation paid" turns a pending status into a completed status. The check is to read the status field on the form; a status of pending review is recorded as unverified.
- Treating a similarity score as a permission filter. A returned score indicates relevance only and does not indicate that the record is authorized for use in copy. The check is to first confirm whether the cited record's datasetId and collectionId fall within the target retrieval scope; the size of the score does not change the authorization judgment.
How to verify the setup
- Input the sample material text and the draft sentence, then compare whether every number and status in the draft has a corresponding statement in the material. If the draft contains "exactly 500g" while the material states only "500g±20g," that part is judged unsupported. If the draft states only "specification 500g±20g, label weight 512g," it is judged consistent with the material. The expected result is a per-fragment supported or unsupported judgment; this is a paper comparison and no run has been performed.
- Input a continue-session request with chatId set to an existing session identifier, and with messages containing only one user question. Compare whether the returned result references history under that chatId. If the returned content is unrelated to the history rounds, check whether chatId is unique and shorter than 250 characters. If the returned content references history, record the actual context scope carried in. The expected observation is either a history-linked result or a result that shows no history linkage; this is a paper check and no run has been performed.
- Input a user question containing an inspection photo link with image recognition turned on. Compare whether the returned result reads the inspection information in the image. If the returned content does not involve image content, check whether the model supports image input and whether the link is extracted when the user question is under 500 characters. If the returned content involves image content, record the actual effect of multimodal recognition. The expected observation is either image content reflected in the result or not; this is a paper check and no run has been performed.
Across all three checks, missing evidence stays missing. A pending request is not an approval, an offered option is not an execution, and an unsigned termination is not a completed termination.