Trusted AI

Trusted AI

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Building a Culture of Responsible AI
How training, incentives and everyday rituals turn trustworthy AI from policy into practice
 • Jon Knisley
Why Trusted AI Programs Stall at the Product Layer
And how to build one that turns trust into adoption rather than friction
 • Jon Knisley
Trusted AI Has a Shelf Life
How to maintain reliability, detect drift, and manage change across the AI lifecycle
 • Jon Knisley
The Feedback Gap
How in-product flags, annotation programs, and RLHF-style pipelines turn feedback into safer, smarter AI systems
 • Jon Knisley
Evaluating Generative AI: Practical Tests for Safety, Bias and Fairness
How to assess toxicity, hallucinations, representational harms and group fairness
 • Jon Knisley
Stop Guessing: How to Evaluate Modern LLM Applications
A practical framework for measuring what matters across prompts, retrieval, and tool behavior
 • Jon Knisley
Your AI Passed Testing. Will It Survive Production?
Experiments, staged rollouts, and runtime guardrails that turn deployment risk into repeatable control
 • Jon Knisley
Why Most LLM Evaluations Fail Before Production
How to build test sets, use synthetic data wisely, and trust LLM-as-a-judge without fooling yourself
 • Jon Knisley
The Wrong Scorecard: Why Most Organizations Are Failing at AI Evaluation
A decision framework for investing evaluation resources where they matter most
 • Jon Knisley
Trusted AI
Trusted AI
Trusted AI examines the governance and observability required to build AI systems that scale. Views expressed are my own.
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