Stay Confident.
The latest developments, best practices and deep dives on AI evals.
Confident AI Prompt Monitoring Guide
Prompt monitoring ties every production LLM call to the prompt version behind it. How to set it up, what to watch, and how to catch regressions.
A Practical Guide to Building the Right Agent Evals
Confident AIA Practical Guide to Building the Right Agent Evals
The right evals start with real production failures, not a list of generic metrics. Here is a practical workflow for discovering issues, validating them with humans, and turning them into durable evaluation coverage.
AI Agent Observability: Everything You Need to Know in 2026
Everything you need to know about AI agent observability in 2026 — traces, spans, and threads; online and offline evals; production monitoring; and closing the feedback loop so failures never repeat.
Human-in-the-Loop Workflows for AI Agent Evaluation: Complete Guide
A practical guide to human-in-the-loop workflows for AI agent evaluation: how SMEs review AI agent failures, align automated metrics, and improve evaluation datasets.
LLM Product Manager Workflows: A Complete Guide to AI Quality
A practical guide to LLM product manager workflows, built around the two things PMs can finally do without waiting on engineering: build on the AI product by editing prompts, running evals, and comparing variants, and monitor quality with dashboards, signals, and shareable evidence.
The Complete Guide to LLM Experimentation: Compare Prompts, Models, and Agents
A practical guide to running LLM experiments across prompts, models, tools, datasets, metrics, production A/B tests, and human-in-the-loop feedback loops.
LLM Evaluation for Startups: The Complete Guide
A practical LLM evaluation guide for startups: build a small dataset, use the 2 + 3 metric rule, run CI/CD evals, and grow coverage from production signals and human review.
Three Ways AI Systems Fail Even When Evals Pass
AI systems can pass evals while still behaving incorrectly. This post explores three common failure modes that slip through output-based evaluation.
Your AI Agent Passed Evals. That’s the Problem.
Passing evals doesn't mean your AI agent works — it means your tests missed how it fails. Why output-based evals create false confidence and what to measure instead.
Multi-Turn LLM Evaluation in 2026: What You Need to Know
In this article, I'll break down multi-turn LLM evaluation — how it differs from single-turn, what metrics actually matter, and how to implement it.