Annotation workflow
Designed the process human annotators follow to review and label model outputs on the platform.
AI Agent · Earlier work
AI Platform — Annotation & Evaluation Workflow
Turning raw model outputs into something a human can annotate — and a process that can tell you when quality slips.
Designed a human annotation workflow and an automated testing/reporting process for model outputs on an AI platform.
- Evals
- Annotation Pipeline
- AI Platform
Overview
This project covered two connected pieces of an AI platform's quality process: designing the workflow human annotators use to label and review model outputs, and setting up an automated process for testing model outputs and generating reports on them.
- Role
- Designed the human annotation workflow and the automated testing/reporting process for model outputs.
Highlights
- Designed a human annotation workflow for reviewing model outputs
- Built an automated testing process for model outputs
- Set up reporting on model output quality
Features
Automated output testing
Set up automated checks that run against model outputs as part of the platform's testing process.
Reporting
Built a reporting process that surfaces the results of automated testing for review.
Process
- Designed the annotation workflow structure for human reviewers
- Built the automated testing process for model outputs
- Set up the reporting flow to surface test results
Challenges
- Designing an annotation workflow that's clear and consistent for human reviewers
- Building automated tests that meaningfully evaluate model output quality
Recorded result
A working annotation workflow for human reviewers and an automated testing/reporting process for model outputs on the AI platform.
Reflection
This project sat on the evals side of AI development — a reminder that shipping a model or agent is only half the work; having a process to actually check its outputs is the other half.