skylar payneStart with a case study
40% Better, 75% Faster describes the query sets, annotations, and performance snapshots used in the Frigade engagement.
Series
Practical AI Evals
Essays and case studies on reviewing AI outputs, identifying failures, and testing improvements.
Who this is for
- Engineers setting up or improving an evaluation process.
- Product teams shipping agents, RAG, onboarding assistants, copilots, or workflow automation.
- Teams deciding what to fix after reviewing production traces.
Essays in this collection
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If you built the agent, why are you extracting its task from chat traces instead of recording typed inputs and outputs at the source?
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A streamlined system for AI evaluation that closes the gap between seeing problems and fixing them.
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How Frigade Slashed Latency & Boosted User Helpfulness
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An AI Maturity Model
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A Practical Guide to Evaluation-Driven Improvement
Where to go next
Use the AI Evals topic hub for more writing on evaluation.