What the documents look like
For document comparison in Near-expiry markdowns, this page examines FastGPT context limits. Inspect candidate lists, markdown advice and approval scope while preserving lot identity and inspection stage. The examples are hypothetical.
Constraints at the context limits step
An invented short-dated-stock agreement lists lots awaiting inspection and proposes markdowns, makes actual handling subject to current-lot approval and separates unchecked items. Only a candidate list and advice note are attached, without approval or current physical-inspection records. History contains handling advice for another lot. Candidate status does not establish inspection findings, and advice does not establish changed prices or disposed goods. Actual input must preserve current-lot approval and unchecked-item restrictions. A short-dated heading does not give every listed item the same status. No real food standard, deadline or markdown figure is supplied.
Configuration and implementation checks
| Short-dated-stock context | Current restriction | Corresponding check |
|---|---|---|
| Candidate list | Includes pending inspection | Does not establish inspection findings. |
| Advice note | Proposed markdown only | Does not establish execution. |
| Current-lot approval | Not supplied | Check lot, goods and scope. |
| Historical advice | Another lot’s matter | Do not automatically apply it here. |
| Unchecked items | Separate group required | Retain grouping in input and output. |
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
The first error drops separate grouping of unchecked items from input. Compare the complete agreement, candidate groups and citation for pending items moved into an actionable group. The second applies another lot’s advice to a current follow-up. Hold current lot identity fixed across history conditions and inspect covered goods and inspection scope. The third labels markdown advice as executed in a structured table. Read the proposed action, approval and actual operation records after parsing. Grouping, lot applicability and execution need separate checks; a price column does not establish approval.
How to verify the setup
Ask which candidates were listed, which approval covers this lot and which operation stage the materials support. Read actual input item by item. Added approval supports covered contents after product and scope checks; execution and physical condition still require corresponding records. Inspect citation tokens separately from reply length and model context. Longer answers do not establish source delivery. This hypothetical exercise determines no saleability, changes no price and destroys no inventory. An absent approval page leaves approval unknown instead of proving it was never granted.
| Material context | Scope |
|---|---|
| Document types | Near-Expiry List, Discount Approval, Spoilage Report and Promotional Price Tag |
| Candidate sources | POS Inventory Age, Store Inspection, ERP Inventory and Quality Inspection Records |
| Technical baseline | FastGPT v4.17.2 |