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LLM Tracing: The Foundation of Reliable AI Applications
Your 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 Systems
As 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 Analysis
As 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 Validation
Imagine 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 Evals
In 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 Practices
The 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 Analysis
Within 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 Detection
Building 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 Production
LLMs 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 Pipeline
LLM training shapes everything from what a model knows to how it reasons and responds. So, understanding how models are…


















