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AI systems can pass evals while still behaving incorrectly. This post explores three common failure modes that slip through output-based evaluation.
Passing evals doesn’t mean your system works. It means your tests didn’t catch how it fails.
Your best evaluation data already exists — it's sitting in Google Drive, SharePoint, Notion, and S3. Dataset generation on Confident AI turns your existing documents into evaluation-ready datasets automatically.
You can't improve what you can't see. Auto-categorization tells you what your users are actually asking, detects response drift, and shows you which categories perform best — and which ones need help.
Production traces are the best dataset you’ll ever get — but most teams never turn them into one. With auto-ingest, your traces flow straight into datasets and annotation queues, continuously.
Everyone agrees evals should run regularly. But nobody remembers to actually run them. Scheduled Evals fixes that — set the frequency, configure your mappings, and never scramble before a release again.
Error analysis used to mean pulling traces in code, hacking together an LLM to recommend metrics, and hoping for the best. Not anymore.
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.
This article will teach you everything you need to evaluate MCP-based LLM applications.
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.