What the documents look like
Store cold-chain receiving for fresh produce relies on four document types: cold-chain temperature records, arrival acceptance sheets, rejection sheets, and supplier delivery notes. These materials originate from vehicle temperature control, WMS, store receiving systems, and quality inspection photos. They update per batch and per day. For copy drafting, the drafter extracts verifiable facts from these materials, such as whether a batch arrived within its temperature range, whether the acceptance conclusion is acceptance or rejection, and whether a rejection reason is stated. Any sentence about product quality, arrival condition, or handling action must map to a field in these materials. Information not recorded in the materials cannot be supplied by the copy itself.
The category differs from fruit procurement: cold-chain receiving focuses on transport temperature and arrival batch, and does not handle origin price inquiries.
Constraints at the context limits step
The model context window is checked against actual model capability. The reply cap is an output-side limit. These two are distinct and cannot be treated as interchangeable. The knowledge base reference limit is measured in tokens; a long material section may be truncated, so the drafter needs to compare against question samples whether referenced content is complete. Memory rounds affect input content: the same question under empty history and under history may produce different reference results. When a fixed reply format is used, field completeness and parse success must be verified; checking text length alone is insufficient. Retrieval similarity scores serve ranking and filtering, and do not perform permission or entity isolation. Libraries built with different vector models cannot be jointly associated; identifiers must be checked first and non-matching ones excluded.
Configuration and implementation checks
The following configuration is organized around the copy drafting task, using the hypothetical drill sample below.
| Configuration item | Hypothetical starting value for this exercise | Reason and comparison method |
|---|---|---|
| AI model | Select a model whose maximum context exceeds the total material token count | Compare total material tokens against the model maximum context to confirm referenced content is not truncated |
| Reply cap | Set long enough to hold the cited basis and the handling action | Compare against the character count of the draft sentences; too short truncates the basis statement |
| Reference limit | Set according to the token size of a single material section | Compare against the longest material section to ensure key fields enter the reference intact |
| Memory rounds | Retain a small number of history rounds | Compare the same question under empty history and under history to observe reference differences |
| Reply format | Default text | Switch to json_schema only when a downstream process requires a fixed structure |
| Minimum relevance | Applies only under semantic retrieval or reranking | Compare against the retrieval score distribution to filter low-relevance fragments |
Hypothetical drill sample: the material text reads: "On 10 March 2025, the cold-chain vehicle temperature record shows an arrival temperature of 2°C. The arrival acceptance sheet states the batch totals 50 boxes. The acceptance conclusion is acceptance." The draft sentence to be assessed is: "This batch arrived under full cold-chain conditions and the product is fresh." When checking this draft sentence, the material records only the arrival temperature and the acceptance conclusion. It does not record a "full" temperature log or a basis for judging "fresh." The draft sentence exceeds what the material supports.
Three common errors
- Treating the reply cap as the full context window. The reply cap controls output length; the context window is determined by model capability. Confusing the two leads to misjudging referenced material as fully loaded. To check, input a material segment longer than the reply cap, compare whether the output is truncated, and observe whether the truncation point falls before the basis field.
- Treating a similarity score as a permission or entity filter. Similarity is used only for ranking and filtering; it does not exclude unrelated business or unauthorized records. To check, input a query containing a different batch identifier, compare whether returned results still contain other batch content, and observe whether cross-batch references appear.
- Treating a pending-review status as approved. When the material's acceptance conclusion is pending review, the draft sentence must not state that the batch was accepted. To check, input material whose acceptance conclusion is pending review, compare whether the draft sentence contains acceptance-type wording, and observe whether the draft sentence matches the material status.
How to verify the setup
- Input the material text and draft sentence from the hypothetical sample above. Compare whether "full cold-chain" and "fresh" in the draft sentence can be mapped to fields in the material. When the material does not record them, the draft sentence should be marked as pending verification.
- Input the same question under empty history. Compare the referenced content under history and under empty history, and observe whether the referenced content changes with the number of history rounds.
- Input a long material segment exceeding the reference limit. Compare whether the referenced content includes key fields such as the acceptance conclusion and the rejection reason, and observe whether key fields are truncated.