Index listing of all 12 items tagged with #evaluation.
Evaluation compares behaviour against an expectation written down first. What to check, which method suits which property, and how to keep changes visible.
What to log in an AI system so its behaviour can be investigated later: how monitoring, audit and replay differ, and where a record stops being proof.
An AI pilot proves the model can do the task on chosen data. Production needs it to work inside the organisation. Why pilots stall, and what gets them through.
OCR sits at the front of every document pipeline. When it misreads a table or a total, every retrieval step and every answer downstream inherits that error.
In LLMOps, evaluations are continuous operational contracts rather than static benchmark milestones.
Measurement must establish a baseline before optimization begins, to avoid scaling noise.
Without traces and verification signals, teams repeat the same failure with new words.
Quality checks must run continuously at runtime to adapt to shifting user inputs.
A small review ritual for checking whether my AI workflows are getting clearer or only getting faster.
A small experiment to see where longer context starts to degrade quality.
Shortlist for building safer, more measurable prompts.
A compact resource pack for checking whether an AI system retrieves the right evidence before it answers.