AI Reliability

Introduction Retrieval-Augmented Generation (RAG) has emerged as a foundational approach for building advanced AI applications that combine the strengths of large language models (LLMs) with external knowledge sources. RAG pipelines empower applications to retrieve relevant information from vast datasets and generate precise, context-aware responses, making them essential for enterprise use-cases,

Most AI agents are still fragile. If you’re building for real users, reliability is non-negotiable. We’ll cover evaluation, simulation, observability, iteration, and security, with clear metrics, examples to level up your stack. TL;DR * Reliability is the non-negotiable for agentic systems. * Measure: task success, tool call errors, loop

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