Advanced AI evaluation

For teams past the basics. Deterministic metrics, error analysis, agent harness evaluation and the failure modes that only show up when you're serving real traffic. Assumes you already have an evaluation loop and want to make it hold up under scrutiny.

Article12 MAR 2026

If the harness is the moat, evaluation is how you defend it

If models are becoming interchangeable and the agent harness is the moat, evaluation is what turns the loop into an advantage you can prove — on your domain's definition of right, all the way through the deep runs where the interesting failures hide.

Catalina Turlea

Article15 JAN 2026

Temperature, max tokens, and streaming: the LLM settings that quietly change your output

The API parameters you send with every prompt change how the model behaves. What temperature, max tokens, top-P, reasoning settings, and streaming actually do — and when they matter.

Madalina Turlea

Article15 JAN 2026

Error analysis for AI: turning messy review notes into a fix list

You cannot improve an AI feature until you know how it actually fails — not how you think it fails. The unglamorous front end of every reliable AI feature: read, annotate, group by root cause.

Madalina Turlea

Article15 JAN 2026

Deterministic metrics: automating the checks you would otherwise do by hand

Small pieces of code that run on every answer and check for something specific — length, format, language, JSON schema, expected items. How metrics measure (not enforce) and connect back to error analysis.

Madalina Turlea

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