ExperimentProposed topic — outline, not published findings

Evaluating AI Systems Beyond Demo Quality

A demo proves possibility once. Evaluation proves reliability repeatedly. The gap between them is the work.

Conceptual inference and evaluation structure

Why demos mislead

Demos use curated inputs and forgiving judges. Production meets messy data, adversarial users, and edge cases the demo never saw.

Building an evaluation practice

Golden sets from real cases, automated scorers plus human review for judgement calls, and regression gates on every prompt, model, or tool change.

What 'good' means

Define acceptable error rates per failure type before launch. A wrong total on an invoice is not the same as a clumsy sentence.

More from the lab

Compact silver and lime layered model architecture

01 · Engineering note

Small Language Models for Specialized Applications

When a smaller, adapted model beats a frontier model — on cost, latency, privacy, and control.

Lab note · proposed topic

Layered computational structures representing model adaptation

02 · Engineering note

Fine-Tuning vs. RAG: Choosing How a Model Learns Your World

Two ways to make a model know your domain — and why the answer is often both.

Lab note · proposed topic

Conceptual agent evaluation gates and verification paths

03 · Engineering note

Designing Reliable AI Agents for Production

Reliability comes from constraints, evaluation, and observability — not from a better prompt alone.

Lab note · proposed topic

Working on something similar?

We enjoy hard problems. Tell us what you're investigating.