Gather Application Scoring Data and AI Logic
Compile all documentation detailing the current application intake process, AI scoring rubrics, prompt engineering logs, and historical submission outputs. Consolidate raw applicant datasets, machine-generated evaluations, and human panel moderation notes into a structured audit pack.
Completing this action establishes a single source of truth for the AI-assisted application screening mechanism. It ensures the evaluation task is grounded in verifiable operational data rather than anecdotal impressions of AI performance.
The founder must present an audited compilation containing prompt system architectures, sample scoring outputs, baseline assessment criteria, and moderation logs. This must clearly link input applicant data to output AI recommendations across a statistically meaningful sample of applications.
Five questions an expert would ask when reviewing your output
Use these to challenge assumptions, pressure-test your logic, and check the quality of this action's output in the context of the parent task and wider venture development.
- 1
What specific criteria were used to select the sample cohort of applications for this review?
- 2
How have you verified that the compiled prompt logs represent the current live production pipeline?
- 3
Where is the raw baseline data showing human assessor scoring prior to the introduction of AI assistance?
- 4
How do you account for potential sampling bias in the application artefacts gathered for this audit?
- 5
Which data privacy and governance permissions govern the reuse of applicant text within this evaluation dataset?