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LLM Tracing: The Foundation of Reliable AI ApplicationsYour RAG pipeline works perfectly in testing. You’ve validated the retrieval logic, tuned the prompts, and confirmed the model returns… 
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LLM Monitoring: From Models to Agentic SystemsAs software teams entrust a growing number of tasks to large language models (LLMs), LLM monitoring has become a vital… 
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Opik Release Highlights: GEPA Agent Optimization, MCP Tool-Calling, and Automated Trace AnalysisAs AI agents and LLM applications grow more powerful and complex, this month’s Opik updates integrate leading-edge technologies to help… 
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Thread-Level Human-in-the-Loop Feedback for Agent ValidationImagine you are a developer building an agentic AI application or chatbot. You are probably not just coding a single… 
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Introduction to LLM-as-a-Judge For EvalsIn recent years, LLMs (large language models) have emerged as the most significant development in the AI space. They are… 
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The Ultimate Guide to LLM Evaluation: Metrics, Methods & Best PracticesThe meteoric rise of large language models (LLMs) and their widespread use across more applications and user experiences raises an… 
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How We Used Opik to Build AI-Powered Trace AnalysisWithin the GenAI development cycle, Opik does often-overlooked — yet essential — work of logging, testing, comparing, and optimizing steps… 
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Release Highlights: Extensive Model Support, New Quick-Start Options & Simplified Insight DetectionBuilding and scaling GenAI applications involves numerous moving parts, from logging your first LLM trace to managing experiments across complex… 
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AI Agent Design Patterns: How to Build Reliable AI Agent Architecture for ProductionLLMs are powerful, but turning them into reliable, adaptable AI agents is a whole different game. After designing the architecture… 
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Pretraining: Breaking Down the Modern LLM Training PipelineLLM training shapes everything from what a model knows to how it reasons and responds. So, understanding how models are… 


















