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ai-development
Company AI Skills & Evaluation System
An internal AI skills and evaluation system that helped teams define, test, benchmark, and improve reusable AI capabilities across business workflows.
The goal
Create a structured system for defining AI skills, testing their performance, and improving reliability before deployment into business workflows.
What we did
- ✓Defined a standard format for documenting AI skills
- ✓Created evaluation criteria for accuracy, completeness, tone, safety, and usability
- ✓Built test scenarios based on real business workflows
- ✓Developed scoring rubrics and review processes
- ✓Added version tracking for prompts, tools, and instructions
- ✓Created dashboards to compare skill performance over time
- ✓Introduced feedback loops for continuous refinement
The result
The system made AI development more measurable and repeatable. Teams could evaluate whether an AI skill was ready for use, identify failure patterns, and improve performance before wider deployment.
As organisations adopt AI across more workflows, one of the biggest challenges is consistency. A prompt or agent may work well in isolated examples but fail when exposed to varied real-world requests. Without structured evaluation, it becomes difficult to know whether an AI capability is reliable enough for operational use.
This project focused on building a company AI skills and evaluation system. The purpose was to define reusable AI capabilities, document how they should behave, and evaluate their performance against realistic scenarios. Instead of treating AI development as informal prompt writing, the system introduced a more disciplined approach to testing and improvement.
Each AI skill was documented with its purpose, expected inputs, required outputs, constraints, examples, and known edge cases. Evaluation scenarios were then created to test the skill across normal, complex, ambiguous, and failure-prone situations. Scoring rubrics measured dimensions such as accuracy, completeness, instruction following, safety, tone, formatting, and practical usefulness.
The system also supported iteration. When a skill failed a test case, the issue could be categorised and used to improve prompts, tool instructions, retrieval sources, or workflow logic. Version tracking made it possible to compare performance over time and understand whether changes were actually improving outcomes.
The result was a more reliable framework for deploying AI across company workflows. It improved confidence, reduced ad hoc testing, and created a foundation for scalable AI governance.
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