AI Reliability

TL;DR AI hallucinations—plausible but false outputs from language models—remain a critical challenge in 2025. This blog explores why hallucinations persist, their impact on reliability, and how organizations can mitigate them using robust evaluation, observability, and prompt management practices. Drawing on recent research and industry best practices, we

The rapid evolution of artificial intelligence has ushered in a new era where AI agents are integral to business operations, customer service, healthcare, finance, and more. However, the difference between an AI agent that drives value and one that undermines trust lies in its reliability. This guide provides a comprehensive,

As AI agents become integral to modern products and workflows, engineering teams face increasing demands for reliability, quality, and scalability. Selecting the right evaluation and observability platform is crucial to ensure agents behave as intended across varied real-world scenarios. This article provides a comprehensive, technically detailed comparison of four leading

1. Why Reliability Is the Real KPI A flashy demo is fun, but the board cares about one thing: consistent, accurate output. A single hallucinated answer can tank user trust faster than you can say “GPT-4.” Many enterprises identify reliability as a core barrier to scaling AI systems across production

TL;DR: As AI systems move from prototypes to production, hallucination detection has become essential to maintaining trust and reliability. This guide explains how teams identify factual drift in model outputs, what criteria define an effective detection tool, and how emerging evaluation frameworks are shaping safer, more transparent AI workflows.