What the documents look like
Store counts and loss control work from actual-versus-book discrepancies and shrinkage. The materials are count sheets, variance analyses, damage write-off approvals and surveillance review records. Supplier settlement documents and supplier accounts payable sit outside this task. These materials are typically updated monthly or quarterly, and comparison requires the same count period, the same store and the same field as the basis.
The contract and transaction material comparison task checks key fields in count sheets and variance analyses against existing records, item by item, to determine whether mutually exclusive values appear under the same field. The context and token step determines how much source material a single model request can carry, which affects completeness when several count documents are compared together. A comparison may bring a count sheet, a variance analysis and a damage write-off approval into one request at the same time. The model maximum context limits how much input content one request can reference, while the reply limit constrains output length only; the two are not interchangeable. The knowledge base reference limit controls the content volume referenced per search by token, and a long single passage may be truncated. When multi-turn conversation is enabled, history turns occupy context space, so the same question produces different input with empty history and with history, and each condition must be checked separately. When json_schema fixes the reply format, field completeness and successful parsing must be verified; checking text length alone does not confirm structural completeness. Semantic retrieval and the minimum relevance score affect recall ordering only, and do not carry permission or entity isolation duties.
Constraints at the context limits step
Hypothetical drill sample: a store count sheet for March 2024 states "frozen goods area count variance -12 items, damage write-off approval number BL-0312, status is pending review"; the variance analysis for the same period and same area records "variance -12 items, cause under investigation." Draft determination sentence: "The frozen goods area variance at this store has received damage write-off approval." Comparison must check the same store, the same period and the same field. The damage write-off approval status is pending review, and no approval effective information is recorded.
The model maximum context and the reply limit are separate settings. The reply limit constrains how long a single answer may be; setting it too low can truncate a reply early. The knowledge base reference limit is set in tokens and controls how much content each search references. Memory turns control how many earlier chat rounds the AI may reference at most. The temperature setting controls answer stability, and a lower value suits rule-bound scenarios. Topp also controls reply randomness, and adjusting temperature and Topp at the same time is not usually recommended. The stop sequence ends output when specified content appears, and ordinary chat generally does not need it. Reply format controls the answer format, and json_schema requires the corresponding format requirements to be filled in.
Configuration and implementation checks
| Configuration item | Hypothetical trial starting point for this drill | Reason for the choice and comparison object |
|---|---|---|
| AI model | Select a model supporting long context | Compare against the combined length of the count sheet and variance analysis to confirm it fits |
| Reply limit | Set to a length that fully expresses basis and handling action | Compare against the number of fields the draft determination sentence needs, to avoid truncation |
| Knowledge base reference limit | Set in tokens, covering the main passage of a single count sheet | Compare against the single-passage length and observe whether it is truncated |
| Memory turns | Keep a small number of history turns | Compare against the empty-history input to rule out context differences |
| Reply format | Choose json_schema when fixed fields are needed | Compare against the parsing result to confirm field completeness |
| Minimum relevance | Keep default or fine-tune against recall results | Compare against recall results; it affects ordering only and not permission |
Three common errors
- Treating pending review status as approval. The damage write-off approval status is pending review, and no approval action is recorded, so the draft determination sentence claiming approval is unsupported. Executable check: input the damage write-off approval source text, compare the status field value, and if it is pending review, the determination sentence does not hold.
- Treating the reply limit as the full model context window. The reply limit constrains output length only, while the model maximum context constrains the input content length that can be referenced. Executable check: input the combined count sheet and variance analysis text, compare its length against the model maximum context, and if it exceeds the limit, part of the content was not included.
- Treating semantic retrieval relevance as permission filtering. The minimum relevance score only filters low-relevance results and does not exclude records without access rights. Executable check: input a query containing different store identifiers, compare the store identifiers in the returned results, and confirm the relevance setting did not isolate entities.
How to verify the setup
- Verification one: input the count sheet source text above together with the draft determination sentence, compare the damage write-off approval status field against the sentence wording. If the status is pending review, the determination sentence does not hold and is recorded as unsupported. This is the expected outcome, not a completed test.
- Verification two: input the same question under two conditions, empty history and three history turns, compare the two input contents, and confirm that key materials are still fully included after history turns occupy context. If key material is missing under history, the memory turn setting needs adjustment.
- Verification three: input an explicit paper empty result [] and compare the downstream node handling design, confirming that downstream does not produce a fabricated comparison conclusion on an empty result. This does not claim that any rule retrieval necessarily returns empty.
Libraries across different vector models cannot be associated together; first check identifiers and exclude non-matching ones, record actual observations, and do not infer runtime results of joint association.