Field notes for people who ship AI.
Long-form, practical and specific. Written by the engineers and red teamers who do this work every day.
LLM evaluation: the complete guide
Metrics, graders, datasets and CI gates for evaluating LLMs and agents in production.
AI guardrails for agents: an architecture guide
Where to put guardrails, what each layer catches, and how to keep latency under budget.
AI red teaming: a field methodology
How to scope, run, score and fix an AI red-team engagement, from threat model to retest.
Prompt injection: detection and defense
Direct and indirect injection, why filters alone fail, and the controls that actually hold.
Safety training data: a buyer's guide
Red-team corpora, preference data and refusal calibration: what to buy, what to build, how to check quality.
Evals vs. guardrails vs. red teaming
Three controls that get confused constantly. What each one does and when you need it.
AI safety glossary
44 terms from adversarial robustness to zero retention, defined plainly.
Find out what your model does before your users do.
A 45-minute briefing with our evaluation team. Bring a use case; leave with a threat model, a draft eval plan and a sense of where you stand.