What the documents look like
For document comparison in General import declarations, this page examines FastGPT context limits. Label-proof review and application to the actual batch are distinct verification subjects. The examples are hypothetical.
Constraints at the context limits step
A proposed import agreement says to check labels against a proof first and verify the batch when applying labels to goods. A review sheet says wording checked, with no application record for the current batch. A completion question needs proof, wording and physical batch preserved together in input. Wording checked supports a review action, not actual application or application to this shipment. The proof may also show layout alone, without establishing label quantities or applicable goods. The answer must retain the scope of the available review rather than completing the whole labelling process.
Configuration and implementation checks
| Context review subject | Specific text condition | Acceptance evidence |
|---|---|---|
| Review subject | Proof wording | Identify what the checked action concerns. |
| Physical condition | Application requires batch check | Retain batch qualification. |
| Historical reference | This version may identify another proof | Check version and original wording. |
| Complete input | All wording present but physical record absent | Separate input completeness from evidence gap. |
| Output review | Proof, wording and physical batch | Check missing item and parsing. |
Documented conditions
| Documented item / implementation check | Condition |
|---|---|
Model context | Use the actual model’s accepted context scope. |
Reply length | The reply limit differs from the context window. |
Knowledge-reference limit | Knowledge citation limits are token-based. |
History-input check | Compare empty-history and populated-history inputs. |
Structured-output handoff | Validate parsing, then inspect support for field values. |
Three common errors
One failure is a citation losing the physical-batch condition and the output declaring completion from wording checked. Another is a historical reference to this version attaching to a different proof, changing the reviewed subject. Compare proof identity, review subject and current-batch records to distinguish truncation from reference mismatch. Check actual model context capacity rather than estimating it from the reply limit. Even when all available wording fits, the missing physical record still requires evidence; a complete input does not establish that the operational work itself was completed.
How to verify the setup
Acceptance questions should cover what was reviewed, whether current-batch application is evidenced and which proof this version identifies. Compare the same question with empty and populated history to check whether proof identity changes. A fixed review output should preserve the proof, wording review, physical batch and missing record as distinct entries, followed by actual parsing checks. If an excessively short reply drops the batch qualification, record an output-information loss. That shortened response cannot establish compliance of the physical application with the stated agreement.
| Material context | Scope |
|---|---|
| Document types | Import Declaration Form, Commercial Invoice, Packing List, Bill of Lading and Contract |
| Candidate sources | International Trade Single Window, Customs Tariff, Freight Forwarder Arrival Notice and Foreign Exchange Payment Slip |
| Technical baseline | FastGPT v4.17.2 |