Learn how to seamlessly embed AI quality checks into your existing development workflow. Automate evaluations with Evals.do to catch regressions and ensure only high-performing models make it to production.
Autonomous agents require a new paradigm of testing. This guide covers the essential strategies and metrics for rigorously evaluating the performance, reliability, and safety of complex agentic workflows.
Move beyond technical metrics and learn to articulate the business value of robust AI evaluation. Discover how investing in AI quality with a platform like Evals.do drives user trust, reduces risk, and improves your bottom line.
Is your AI function truly helpful? This post explores the nuanced metrics beyond accuracy, from factuality and latency to tone and safety, and shows you how to measure them effectively.
Manual testing of AI outputs is slow, biased, and unscalable. Explore how to use model-based grading to automate the evaluation of your AI workflows, providing faster and more consistent feedback.
Adopt a new development methodology for AI. Learn how writing your evaluation criteria as code first can guide the creation of more robust, predictable, and high-quality AI services from the ground up.
The quality of your evaluation is only as good as your test data. This article provides a practical framework for creating, curating, and managing high-quality datasets to test your AI components rigorously.
Subjective qualities like helpfulness, tone, and brand alignment are critical for user adoption. Learn how to design and run repeatable experiments to quantitatively measure these qualitative aspects of your AI.
A model update that improves one capability can silently degrade another. Discover why continuous evaluation is non-negotiable for AI services and how to implement it to catch performance regressions early.
Get started with AI evaluation today. This step-by-step tutorial walks you through setting up your first evaluation for a customer support agent, defining metrics, and analyzing the results.