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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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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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AI Assisted Coding with Cursor AI and Opik
How AI can help you move beyond vibe coding and become an effective AI engineer faster than you think Dear…
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Release Highlights: Discover Opik Agent Optimizer, Guardrails, & New Integrations
As LLMs power more complex, multi-step agentic systems, the need for precise optimization and control is growing. In case you…
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Announcing Opik’s Guardrails Beta: Moderate LLM Applications in Real-Time
We’ve spent the past year building tools that make LLM applications more transparent, measurable, and accountable. Since launching Opik, our…
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From Observability to Optimization: Announcing the Opik Agent Optimizer Public Beta
At Comet, we’re driven by a commitment to advance innovation in AI, particularly in the realm of LLM observability. Our…
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Major Releases: MCP Server & Google Agent Dev Kit Support
We’ve just rolled out two major updates in Opik, Comet’s open-source LLM evaluation platform, that make it easier than ever…
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SelfCheckGPT for LLM Evaluation
Detecting hallucinations in language models is challenging. There are three general approaches: The problem with many LLM-as-a-Judge techniques is that…
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LLM Hallucination Detection in App Development
Even ChatGPT knows it’s not always right. When prompted, “Are large language models (LLMs) always accurate?” ChatGPT says no and…
Open-source AI observability & optimization
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Category: LLMOps
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