Guides

TLDR: Long-context LLMs often miss information placed mid-sequence (“lost in the middle”), driven by positional biases like RoPE decay. Production fixes: two-stage retrieval (broad recall + cross-encoder reranking), hybrid search (semantic + BM25), and strategic ordering (top evidence at start and end). Strengthen chunking with contextual retrieval; keep only the most relevant

TL;DR AI agents are failing in production at alarming rates, with over 40% of projects expected to be canceled by 2027 due to inadequate testing and unclear business value. Recent benchmarks show frontier models failing basic tasks up to 98% of the time. This article explores why traditional testing

TL;DR Building trustworthy AI systems requires comprehensive evaluation frameworks that detect hallucinations and ensure model reliability across the entire lifecycle. A robust evaluation stack combines offline and online assessments, automated and human-in-the-loop methods, and multi-layered detection techniques spanning statistical, AI-based, and programmatic evaluators. Organizations deploying large language models need

TL;DR AI agents present unique diagnostic challenges due to their non-deterministic behavior and autonomous decision-making capabilities. Microsoft's AI Red Team catalogued failures in agentic systems through internal red teaming and systematic interviews with external practitioners, identifying security failures that result in loss of confidentiality, availability, or integrity,

Modern AI applications (chatbots, copilots, RAG systems, and voice agents) live and die by the quality of their evaluations. If you cannot trust your evals, you cannot trust your releases. The most reliable way to achieve trustworthy AI evaluation is to curate a high-quality “golden dataset” that mirrors production reality,

Multi-agent systems have become the standard architecture for complex AI applications. However, as these systems grow more sophisticated, understanding their behavior in production becomes increasingly challenging. Without proper observability and evaluation, teams face issues ranging from unexpected agent handoffs to degraded response quality, problems that only surface after deployment. This