This repository demonstrates practical, entry-level AI operations and quality work through a combination of data preparation tools and documented output quality reviews.
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data-ops/
Python scripts used to prepare text data for AI annotation workflows and apply basic, explainable quality checks. This folder represents the primary operational tooling in the repository. -
output-quality-reviews/
Markdown-based examples documenting common AI output quality issues such as factual inaccuracies, missing attribution, overconfident language, and unsupported generalizations. These documents demonstrate how quality issues are identified and documented using consistent, human-readable criteria.
The repository shows how AI operations and quality tasks are handled in practice:
- simple tools support data preparation and review,
- findings are documented clearly and consistently,
- and judgment is applied using explainable, repeatable criteria.
This is not a machine learning project, an AI product, or a research framework.
Reuben Empere
Lagos, Nigeria (UTC+1)
github.com/empere-tech