What the documents look like
Complaint and regulatory reply work draws on four document families at the same time: complaint tickets, regulatory inquiry letters, order evidence, and rectification reports. The material arrives from the 12315 platform, platform complaint centres, customer service chat, and order systems. The people handling it are usually customer service supervisors, legal and compliance staff, and store owners. An error in drafted wording can lead to an administrative investigation, a platform penalty, or reputational damage, so every promotional sentence needs support in the underlying material.
Data here updates irregularly and case by case. For the context and token step, the central constraint is that the maximum context of a model limits how much content a single request can reference, while the reply limit controls output length. These two settings are separate. The knowledge base reference limit is counted in tokens, and in a mixed knowledge base the chunk lengths differ widely, so a token-based limit is more stable than a count of items. The number of memory rounds affects whether the model can use earlier conversation, and a value that is too small may leave earlier information unused.
Two or three of these facts shape this page directly. First, complaint material is multi-document, so the context window must hold a ticket description, an inquiry letter body, order screenshots, chat records, and a rectification draft together. Second, a low reference limit can cut away key evidence before drafting begins. Third, a low reply limit can truncate a rectification reply mid-sentence. The irregular update rhythm does not change any setting on this page.
Constraints at the context limits step
Drafting product or marketing copy in this category means extracting quotable statements from the case material and then producing a draft sentence. Question optimisation adds one model call before retrieval, which improves retrieval across consecutive turns and also adds latency. Minimum relevance is a filter threshold and applies only under semantic retrieval or result reranking; it does not perform permission or entity isolation. The same-vector-model requirement for joint retrieval applies to knowledge base retrieval itself and does not extend to external business registration sites or other systems. RRF is the fusion step for mixed retrieval results, not a standalone switch that can be turned on. A thinking budget is a reasoning budget and cannot be reused as a reference token limit.
Hypothetical paper exercise sample. An e-commerce store receives a complaint ticket forwarded by the platform. The ticket states: "The consumer claims the product page displayed 'lowest price across the entire web', while the actual transaction price was higher than the same item on another platform." The order evidence shows the product page wording as: "This store's same item has a recent transaction price not higher than other platforms." The regulatory inquiry letter asks the store to explain the basis for the promotional statement. The draft sentence to be judged is: "The product page statement 'lowest price across the entire web' is factual." The material text available for comparison consists of the ticket sentence "The consumer claims the product page displayed 'lowest price across the entire web'" and the order evidence sentence "This store's same item has a recent transaction price not higher than other platforms." The draft sentence and the material text differ on the promotional wording, and the phrase "lowest price across the entire web" does not appear in the material.
Configuration and implementation checks
The settings below relate to the sample above and cover only documented settings or implementation checks tied to this feature and primary task. The values are starting points for this hypothetical exercise, used for comparison and observation; they are not official defaults.
| Configuration item | Hypothetical starting point for this exercise | Reason and comparison method |
|---|---|---|
| AI model | Select a model whose maximum context covers the case material | Compare the model's maximum context against the total token volume of ticket, inquiry letter, order evidence, and rectification draft |
| Reply limit | Enabled, set to hold the full rectification reply | Compare against the rectification draft length and observe whether the reply is truncated |
| Memory rounds | Keep a small number of history rounds | Compare against the independence of single-case material and confirm whether earlier turns are needed |
| Reference limit | Set in tokens to cover key evidence passages | Compare against individual passage lengths and observe whether key evidence is cut |
| Minimum relevance | Set as needed under semantic retrieval or reranking | Compare against retrieved content relevance and observe whether low-relevance fragments are filtered |
| Reply format | Plain text, or json_schema when a fixed structure is required | Compare against downstream reading needs and confirm fields are complete and parsing succeeds |
Three common errors
- Treating the reply limit as the whole context window. The reply limit controls output length; the maximum context controls how much content a single request can reference. These are different. If the reply limit is raised while the context limit is ignored, long material may not fit into the request at all. To check, compare the model's maximum context against the total token volume of the material and observe whether it is exceeded.
- Treating minimum relevance as a permission or entity filter. Minimum relevance is a filter threshold and does not exclude unrelated businesses or unauthorised records. Retrieval is not authorisation. To check, run the same question against libraries that differ in access rights and observe whether returned content changes with those rights; if it does not change, the setting does not carry a permission function.
- Treating RRF as a standalone switch. RRF is the fusion step for mixed retrieval results and is not a configuration item. To check, confirm the search mode is mixed retrieval, observe whether multiple result paths are fused and ordered, and do not write RRF into the configuration table as a switch.
How to verify the setup
- Enter the ticket sentence and the order evidence sentence from the hypothetical sample above, then compare the draft sentence "The product page statement 'lowest price across the entire web' is factual" against the material text. The material does not contain the phrase "lowest price across the entire web", so the draft sentence is unsupported. If the draft sentence is changed to "This store's same item has a recent transaction price not higher than other platforms", it matches the order evidence sentence and is supported.
- Enter an empty reference result [] and check the downstream handling design. Observe whether the downstream node has an explicit branch for empty results, and do not claim that a fictional rule retrieval necessarily returns []. If the downstream directly concatenates the empty result into a reply, the handling design is incomplete.
- Check cross-model retrieval association. First inspect whether the vector model identifiers of the two libraries are the same, and exclude any that do not match. Do not run cross-model retrieval to compare missed recalls, and do not invent the runtime result of a joint association. If the identifiers match, record the actual observation; if they differ, record the mismatch and stop the association.