Franchise store supervision: retrieval for staff training

Review retrieval for staff training in Franchise store supervision. Check document scope, failure paths and source evidence through controlled tests.

What the documents look like

Franchise supervision materials centre on four document families: store visit forms, franchise contracts, rectification notices and display standards. They arrive from several sources, including a supervision app, POS sales records, the franchise system and store photographs. Headquarters is concerned with whether franchisees carry out the standards, so supervision records, contract clauses and rectification status are linked across documents rather than contained in one file. Updates follow a weekly and monthly rhythm, and a retrieval request often faces several records for the same store at different points in time. Retrieved passages carry states such as signed, pending review or rectified, and those states come from the source material. The retrieval step only returns relevant passages; it does not decide any of those states.

A second practical feature is that the same store can appear in a contract attachment, a visit form and a rectification notice whose dates and subjects must be matched before any conclusion is drawn. A passage about an authorization period says nothing about whether a rectification was completed, and a rectification notice that states a deadline says nothing about whether the deadline was met.

Constraints at the retrieval step

The knowledge base search node can associate one or more knowledge bases that use the same vector model. Semantic retrieval relies on vector distance, full-text retrieval suits exact queries such as numbers and proper nouns, and hybrid retrieval combines both paths through RRF. Result reranking uses a ReRank model for a second ordering. The reference limit is measured in tokens, and the minimum relevance value takes effect only in semantic retrieval or when result reranking is used. Question optimization performs reference resolution and question expansion, and it adds one model call before retrieval. Image retrieval requires a corresponding index at ingestion time and the relevant model capability. Retrieval and similarity scores carry no permission or entity-isolation role. The cited content is output as an array whose length can be zero.

Hypothetical paper exercise sample. A franchise contract attachment reads: "Party A authorizes Party B to use the brand mark at the store at No. 12 XX Road; the authorization period runs from 1 January 2024 to 31 December 2024; Party B shall submit the previous month's display photographs by the 5th of each month." A rectification notice reads: "A March 2024 store visit found missing temperature records for the refrigerated cabinet; the store is required to backfill the records and submit rectification photographs by 31 March." A draft sentence under review reads: "The store remains within the valid brand authorization period in April 2024." Checking this sentence means comparing it with the authorization period field in the contract attachment. April falls outside the range ending 31 December 2024, so the sentence is not supported.

Configuration and implementation checks

Configuration itemValue or checking action for this pageBasis and explanation
Associated knowledge basesAssociate the bases holding store visit forms, franchise contracts, rectification notices and display standards; check that the vector model identifiers matchThe node requires the same vector model
Search modeFor a question such as "authorization period at No. 12 XX Road", compare semantic, full-text and hybrid resultsCompare per test question; no single mode is presumed suitable for all material
Reference limitFor this hypothetical trial, start at 3000 tokens and compare content completeness against 2000 and 4000The starting value is hypothetical, not an official default
Minimum relevanceLabel relevant and irrelevant samples first, then adjust the threshold and observe which results are filteredTakes effect only in semantic retrieval or with result reranking
Question optimizationFor a follow-up such as "what is the second point", compare the subject in the original question and the optimized questionUsed for reference resolution and question expansion
Result rerankingConfirm a ReRank model is available and enabled in the settings before comparing orderingNo rerank score is produced when it is not enabled

Three common errors

  1. Treating a retrieval hit as authorization confirmation. Input: the store authorization question for No. 12 XX Road. The retrieved contract attachment passage states only the authorization period field. It does not confirm that authorization was valid in April 2024, and signing and effective-date material must be checked separately.
  2. Treating a pending rectification notice as completed rectification. Input: the rectification notice text. The line requiring backfill by 31 March is a requirement, and without a backfill completion record the rectification cannot be judged complete.
  3. Associating knowledge bases that use different vector models. Input: the vector model identifiers of two knowledge bases. When the identifiers do not match, exclude that base and record the actual observation; do not run a cross-model retrieval to compare missed recalls.

How to verify the setup

  1. Input the hypothetical contract attachment text and the draft sentence "The store remains within the valid brand authorization period in April 2024". Compare the start and end dates in the authorization period field. When the draft sentence falls outside the range ending 31 December 2024, record it as not supported.
  2. Input the rectification notice text and the request "confirm that this store's rectification is complete". Compare the requirement item in the notice against the presence of a backfill completion record. When no completion record exists, record the item as pending verification.
  3. Input the vector model identifiers of two knowledge bases and compare whether the identifiers match. When they do not match, exclude that base and record the actual observation. Separately, use a clearly paper-based empty array input to check how the downstream design handles empty references.