1 min read
Data Quality Work That Actually Sticks: separating incident response from root-cause fixes
I tightened system boundaries so quality checks trigger earlier, catching regressions before downstream systems consume bad data.
Quality
12 articles
1 min read
Observability for Data Products: turning data quality from blame into process
I spent the day reducing cognitive overhead for engineers and analysts—introducing clearer table contracts, simpler failure modes, and concise runbooks that…
1 min read
Observability for Data Products: using failure budgets for data reliability
I tightened system boundaries so quality checks trigger earlier, catching regressions before downstream systems consume bad data.
1 min read
Data Quality Work That Actually Sticks: choosing the minimum useful set of quality signals
I focused on making delivery decisions auditable and repeatable—documenting intent, success criteria, and rollback paths to reduce tribal knowledge.
1 min read
Operational Data Quality Notes: separating incident response from root-cause fixes
I spent the day reducing cognitive overhead for engineers and analysts—introducing clearer table contracts, simpler failure modes, and concise runbooks that…
1 min read
Data Quality Work That Actually Sticks: choosing the minimum useful set of quality signals
I worked on smoothing the handoff between data engineering and AI teams—standardizing feature contracts, embedding validation, and adding lightweight…
1 min read
Testing AI Systems: What Actually Works
Traditional testing assumes deterministic behavior. AI systems are probabilistic. Same input, different output.
1 min read
Continuous Evaluation: Monitoring AI Quality in Production
Continuous evaluation maintains AI quality standards throughout the system lifecycle.
1 min read
Evaluation Metrics for AI: Measuring What Matters
Regular evaluation with consistent metrics drives continuous improvement in AI quality.
4 min read
Quality-Based LLM Routing: Maximizing Output Quality
Quality isn't one dimensional. Consider: Accuracy : Factual correctness Completeness : Covering all aspects Coherence : Logical flow and consistency…
1 min read
LLM Evaluation Metrics: Measuring What Matters
Understanding and implementing key metrics for evaluating LLM application performance and quality.
1 min read
Azure DevOps Test Plans: Quality Assurance at Scale
Automated tests are the bulk of my testing pyramid, but there's a tier you can't replace: the human running through a workflow looking for the thing nobody…