What this category's data looks like
This category's data looks like this
The formulation of talent cultivation programs in vocational colleges involves three types of materials: talent cultivation programs, course standards, and job competency analysis tables. Fields include program name, course name, class hours and credits, job title, competency requirements, formulation date, and signature fields. The material sources are academic affairs offices, secondary colleges, and industry-enterprise research. The update cadence is triggered by academic year or program adjustments, with many versions and signature receipts easily missing. During multilingual materials and terminology processing, the talent cultivation program provides the program and course framework, the course standards provide terminology definitions and class-hour conventions, and the job competency analysis table provides job terminology and competency verbs. After the three enter the vector model and index, each serves as a recallable raw data fragment, supporting subsequent terminology comparison and the generation of comparison drafts.
Configuration and implementation checks
This step is executed by the indexing service and vector database, outputting vectors and index status (whether recall is possible, whether data in the same group is merged). "Unified terminology glossary and comparison draft" is then completed by subsequent AI nodes, code nodes, or manual work (retrieval and citation trimming only take supporting materials).
| Object | Check | Verification action |
|---|---|---|
| Talent cultivation program | The program name, course name, class hours and credits, formulation date, and signature fields are written consistently in the course standards and job competency analysis table | Sample one program record, check the program name, number of courses, and formulation date in the three materials, and check whether the signature fields and receipts are complete |
| Course standards | Terminology definitions and translated names can be traced back to the original text, and the same term is written consistently throughout | Open one course standard data row, check whether the index field contains multiple vector identifiers, and search the term to see whether data in the same group is merged |
| Job competency analysis table | Job titles, competency verbs, quantities, and dates match across the two materials | Compare against the job cluster description in the talent cultivation program, check the job titles and quantities, and review the version number and signature receipts |
| Data row index field | Multiple vector identifiers for one piece of data are all present, and the index model matches the vector model actually used by the knowledge base | Check the index model in the knowledge base settings, open the data row to check the index field, and review the data scope and status value indexed covered by the rebuild task |
Three common errors
- Symptom: The same program name is written differently in the talent cultivation program and the course standards. Inspect the following: The versions of the two materials are not synchronized, the terminology glossary is not unified.
- Symptom: The number of jobs and the formulation date do not match across the two materials. Inspect the following: After the job competency analysis table was updated, it was not written back to the talent cultivation program, signatures or receipts are missing, and the raw data was not merged.
- Symptom: Duplicate entries appear in retrieval results, and recall quality drops after changing models. Inspect the following: A data row has content but cannot be retrieved, rebuild is triggered only for data that has already been indexed, and data in the same group is not merged by the highest score.
How to verify the setup
Acceptance for this feature: sample one data row to inspect the index field, confirm that multiple vector identifiers are all present, then retrieve once to see whether data in the same group is merged; check that the index model matches the vector model actually used by the knowledge base, review the data scope covered by the rebuild task records, recognize only indexed and rebuild task records as status values, record the citation count separately by array length, and verify the tokens of the cited text separately from the budgets related to limit.
Business test: sample three terms to check whether their spelling is consistent throughout, verify that translated names can be traced back to the original text, and then compare the names, quantities, dates, and signature receipts in the talent cultivation program, course standards, and job competency analysis table.
- In the knowledge base settings, check the index model, open one data row to check the index field, confirm that multiple vector identifiers are all present, and review the data scope covered by the rebuild task
- Sample three terms to check whether their spelling is consistent throughout, compare the names, quantities, dates, and signature receipts in the talent cultivation program, course standards, and job competency analysis table, and record the array count and cited text tokens