Discover how to move past simple accuracy scores. Learn to define and measure crucial metrics like helpfulness, tone, and safety to build truly reliable AI agents with Evals.do.
Poor AI performance can damage customer trust and your bottom line. This post explores the ROI of implementing a robust evaluation strategy and how it directly impacts business success.
Learn how to automate AI quality control. This guide walks you through integrating Evals.do into your existing CI/CD workflow to catch performance regressions before they hit production.
Your evaluation is only as good as your test data. We discuss best practices for creating, managing, and versioning high-quality datasets for consistent and reliable AI testing.
Evaluating a single prompt is one thing, but what about a multi-step agentic workflow? We break down strategies for scoring complex AI processes from end to end using Evals.do.
Which prompt is better? Which model performs best for your use case? Learn how to set up systematic experiments and compare AI component performance head-to-head to find the optimal configuration.
Great agents are built from great components. Understand why granular evaluation at the function level is key to debugging and improving overall AI system performance and reliability.
Human evaluation is the gold standard but doesn't scale. Learn how to effectively configure and use 'LLM-as-a-judge' within Evals.do to automate and scale your qualitative assessments.
A real-world look at how continuous evaluation helped a team identify and fix an AI agent's tonal inconsistencies, leading to a 40% improvement in tone score and higher customer satisfaction.
Understand how a dedicated evaluation platform complements popular AI development frameworks, providing the missing testing and quality assurance layer for production-ready applications.