Strategic thinking on AI quality and operations.

How to think about, plan for, and operate AI quality at scale. Observability, risk, cost, and the principles that hold the discipline together.

Dec 2025 12 min

Reducing Risk in Token Cost and Performance in AI Systems

Managing system stability and business cost. Model providers measure usage in tokens, this strategy covers how to measure and control both costs and system performance.

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Dec 2025 8 min

Observability

Turning the LLM black box into a white box. Observability is the practice of collecting input, output, and system data to enable root cause analysis and behavioural debugging.

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Nov 2025 7 min

Statistical Risk

Use statistics to assess risk. Measure risk statistically by applying a risk rubric with scoring for probability and impact.

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Nov 2025 6 min

Evaluation Over Testing

Testing was built for systems that give the same answer twice. LLMs do not. Evaluation is the practice that fills the gap, replacing pass and fail with better and worse.

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Nov 2025 5 min

More is Better

Sending in more requests leads to better evaluation. Testing for stability and consistency, not just correctness, is fundamental to evaluating non-deterministic systems.

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Nov 2025 7 min

Is There a Role for AI Quality Engineers?

The AI Engineer job spec covers everything from fine-tuning to RAG pipelines to prompt design. As AI infrastructure scales, specialism is inevitable, and quality engineering is where the biggest gap sits.

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Coming Soon

Upcoming 9 min

Ethical Considerations in AI Quality

Exploring the ethical dimensions of AI quality engineering, from bias detection to fairness metrics to responsible disclosure.

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