Resource Hub
Cost per successful task: Benchmarking Kimi K3, GPT-5.5, and 8 more AI models
Arize and Fireworks benchmarked 10 AI models across 2,400 agent runs. Learn why cost per successful task beats token price for model evaluation and routing.
Agent harnesses: How to trace, evaluate, and improve AI agents
Learn how agent harnesses use tracing and evaluations to make AI agents observable, testable, safer, and easier to improve in production.
How to measure human-LLM judge alignment
No single metric proves an LLM judge is trustworthy. This field guide shows how to measure human–human agreement, compare it to LLM–human agreement, and diagnose errors with precision, recall, and F1.
The definitive guide to LLM evaluations
LLM evaluation: Get from pre-production to deployment with our definitive guide to LLM evaluation. Includes LLM eval types, use cases, templates and tips for continuous improvement.
How OpenAI uses human feedback to evaluate and improve LLMs
At ChatGPT scale, user frustration arrives as support tickets, ratings, social posts, and corrections buried inside conversations. OpenAI built a feedback system that can find the pattern behind a complaint, retrieve the evidence around it, and send an agent toward the code.
AI agent evaluation: An agent-native framework
Learn how to evaluate AI agents across outcomes, trajectories, decisions, and repeated-run reliability using traces, checks, and LLM judges.
Inside Cursor’s agent factory: how it verifies AI-written code
As background agents take on more implementation work, Cursor is rebuilding the software development lifecycle around risk scores, developer-like environments, video evidence, and review systems that learn from every human correction.
Why do you need evals for AI agents?
Agent failures hide inside correct-looking outputs. Learn why evals are the only mechanism that catches them, and how to wire them into dev, CI, and production.
LLM-as-a-Judge: When should you use it?
LLM judges fit a narrow window. Learn the specific conditions that qualify a task, three prerequisites, and when a regex or compiler is the better choice.
What is AI engineering?
AI engineering takes a capable model as a given and builds a reliable product around it.
The hidden economics of LLM evaluation costs
Learn what drives LLM evaluation costs across offline tests and production, from judge tokens and trace volume to agent workflows, routing, and release decisions.
Kiro CLI observability: trace and evaluate agent changes with Arize Skills
Use Arize Skills with Kiro CLI to trace coding-agent changes, build datasets from failures, run experiments, and validate prompts before shipping.
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