23 stories
Enterprises are increasingly hesitant to give AI agents carte blanche ability to deploy code or system changes to production without human approval, even as they continue investing in tools designed to evaluate agent reliability.Read more
Ask a judge model the same pairwise question 50 times and you’d expect 50 identical verdicts. In a 2026 study of GPT-4o-mini and GPT-4.1-mini as...
An LLM can return a fluent, confident answer that contains unsupported claims, contradicts the retrieved context, or invents facts. Traditional application monitoring won’t catch any...
An AI system can give a confident answer its source data doesn’t support. It can retrieve the wrong document and summarize it fluently, pick the...
An LLM can return valid JSON that your application still can’t use. It can accept a malicious prompt without complaint, break a schema on the...
VentureBeat Intelligence surveyed respondents at organizations with 100 or more employees in its August VB Pulse survey. We asked about AI agent evaluation and production failures, whether agents may push changes to production with no human review, and which evaluation platforms enterprises…
RAG evaluation metrics with formulas and a worked example: precision@k, recall@k, MRR, nDCG, faithfulness and answer relevancy, plus why frameworks disagree.
The 11 best AI evaluation tools for production in 2026, ranked by seven criteria checked in vendor docs: online scoring, CI gates, alerts and self-hosting.
A one-line prompt change can ship an agent that tells customers the wrong refund window, and nothing in a normal CI pipeline checks what the model says.
An agent passes every offline eval in CI. You ship it. Production breaks within a week, because a class of prompts your eval dataset never...
Most teams sample 1% of production traffic for online evals and classification because every judge call costs money. Jev-as-a-Judge makes those decisions fast and cheap enough to run on 20%, 50%, or more of your traces, so every workflow on Confident AI has more data to work with.
confident-trace is our new Python and TypeScript SDK for tracing production AI applications. Here's why we're separating it from DeepEval and what that means for your stack.
A buyer's comparison of seven LLM evaluation tools: what each one meters, whether it scores curated datasets or live production traffic, what its license actually permits, and how much of your setup survives a switch.
A practical guide to Langfuse alternatives in 2026: what each one is licensed under, what its meter counts, who it suits, and when staying on Langfuse is the better call.
Evaluation measures performance using metrics like precision, recall, and relevancy, providing a clear picture of your pipeline’s strengths and weaknesses using LLMs or ground-truth labels.
As AI spreads across an org, every team evaluates differently and no one can answer 'is this ready to ship?'. AI Governance is the layer on top of the evals, observability, and red teaming your teams already run — turning those signals into one standard, enforced at deploy time.
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.
Use this AI persona deployment checklist to prevent shadow AI, enforce access rules, validate accuracy, and roll out reliable AI personas.
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