What the documents look like
Brand livestream operations place scriptwriting, inventory, and after-sales responsibility on the brand itself, rather than on a commissioned creator. That allocation shapes what contract and transaction material comparison has to handle. The working documents are livestream scripts, product explanation cards, shift schedules, and livestream reviews. Two relationships require per-session checking: explanation cards against transaction details, and shift schedules against session records. Data updates each session or each hour, so the same product can retain separate explanation card versions, price statements, and inventory figures across sessions. Document parsing and chunking must therefore preserve document identity and version information that can be traced back to the source text. Without that traceability, a later comparison cannot confirm which session's material it is relying on.
Constraints at the document chunking step
Segmentation completeness directly affects comparison conclusions. Paragraphs should be checked against the structure of the specific material, so that conditions, notes, and the content they constrain are not split apart. In an explanation card, if the sentence "this price applies to a specific session" and the price value fall into different data chunks, retrieval can only return one of them, and the comparison loses its valid scope. Data and indexes must be distinguished: one set of original data can map to multiple indexes, and an index hit does not mean the original data is complete. Sample retrieval should be run separately with terminology, identifiers, and natural language, observing how segmentation affects the actual comparison question. After material changes, the actual training and index state must be checked; a successful upload cannot be treated directly as retrieval being updated. Semantic recall and relevance scores do not perform permission or entity isolation, and the requirement that a retrieval library use the same vector model cannot be extended to require the same model for business registration sites or other external systems.
Hypothetical drill sample: a brand livestream team retains one explanation card source text reading "This session's product identifier A-001, explanation price 199 yuan, applicable date March 1 single session; this price does not apply to March 2 and later sessions." A separate draft sentence awaits judgment: "A-001 is executed at 199 yuan for the March 2 session." The comparison must confirm whether the subject, identifier, date range, and price field agree. The draft sentence conflicts with the source text's applicable date range, so it is unsupported, and the evidence gap must be recorded without inventing authorization or effective clauses.
Configuration and implementation checks
| Configuration item | Hypothetical starting point for this trial | Reason for choice and comparison method |
|---|---|---|
| Search mode | Hybrid retrieval | Identifier A-001 is an exact query, while natural-language phrasing is a semantic query; both input types need coverage |
| Reference limit | One starting value measured in tokens | The official documentation controls the reference limit in tokens; compare against the total length of same-session material before adjusting |
| Minimum relevance | One starting value between 0 and 1 | Takes effect only in semantic retrieval or when result reranking is used; compare against recall content relevance scores |
| Question optimization | Not enabled for this sample | The draft sentence has a complete subject; enabling it adds one model call and latency |
| Segmentation check | Inspect paragraph by paragraph against explanation card structure | Confirm the price value and applicable date range sit in the same data chunk |
| Post-update review | Check training and index state after changes | A successful upload and retrieval being updated are two separate facts |
Three common errors
- Treating a successful upload as retrieval being updated. Input is the changed explanation card; the comparison object is the actual training and index state; the observable result is that retrieval still returns the old version, which shows the update did not take effect.
- Treating a semantic recall score as permission or entity filtering. Input is a cross-subject retrieval request; the comparison object is the subject identifier on returned records; the observable result is that non-target subject records appear, which shows the relevance setting does not perform isolation.
- Treating a pending-review status as missing operation authority. Input is a handover request marked pending review; the comparison object is three separate facts—signature, cooperation reached, contract effective; the observable result is that a pending-review mark alone cannot establish missing authority or an effective contract.
How to verify the setup
- Input the hypothetical sample source text and the draft sentence; compare the identifier, date range, and price field; observe the result: the draft sentence's applicable date conflicts with the source text range, so it is judged unsupported, and the evidence gap is retained.
- Input product identifier A-001 and a natural-language phrasing once each; compare whether the recalled content contains the price and applicable date in the same paragraph; observe the result: only when both appear together is the segmentation completeness condition satisfied.
- Input an explicit paper-based empty result []; compare how the downstream design handles an empty reference; observe the result: the downstream follows the empty-reference branch, without claiming that retrieval under invented rules necessarily returns []. Libraries using different vector models cannot be associated together; check identifiers first, exclude non-conforming ones, and record the actual observation.