Confident AI

A practical guide to evaluating AI agents with LLM metrics and tracing-plus when human review matters, how it calibrates judges, and workflows that combine CI, sampling, and production signals.

Meibel secures $7M in seed funding led by Mosaic General Partnership to enhance its AI runtime platform, enabling technical teams to control AI behavior in production.

Most LLM evals fail because their metrics don't predict ROI. This playbook shows how to build outcome-based evals that correlate with real business KPIs and user value.

Confident AI raised an oversubscribed $2.2M seed round in 5 days. Here's the fundraising strategy, the investor conversations, and the hard lessons from the raise.

Evaluate LLM chatbots with metrics for relevancy, coherence, and safety, plus multi-turn conversation testing that measures quality across a full dialogue, not one reply.

LLM security risks span prompt injection, data leakage, and insecure output handling. Learn the major risk pillars and the practical techniques to mitigate each one.

Everything you need to generate realistic synthetic datasets with LLMs: data evolution techniques, quality filtering, and code to build datasets from scratch.

A step-by-step guide to building a robust, scalable LLM evaluation framework from scratch - metrics, test cases, and architecture, with DeepEval code examples.

MMLU, HellaSwag, BBH, and beyond: what each top LLM benchmark measures, its limitations, and why these scores matter when choosing a model to build on.