7 stories
A practical guide to running LLM experiments across prompts, models, tools, datasets, metrics, production A/B tests, and human-in-the-loop feedback loops.
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.
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.
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.
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.
RAG evaluation metrics — answer relevancy, faithfulness, and contextual relevancy — measure retrieval and generation quality, with working DeepEval code examples.
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.
Showing 7 of 7 stories
You're all caught up.
Get the top AI news and tools straight to your inbox every week.
Weekly · AI news from Confident AI (DeepEval)
Join 15,000+ AI enthusiasts
