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RAG Evaluation & Monitoring: Metrics, Frameworks & Production Observability

RAG Evaluation & Monitoring: Metrics, Frameworks & Production Observability Comprehensive guide to evaluating and monitoring Retrieval-Augme…

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AI Model Evaluation: Best Practices for Testing and Validation Evaluating AI models is critical for ensuring quality, safety, and reliability. In th…

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You cannot improve a RAG system you are not measuring, and "I asked it five questions and it seemed fine" is not measuring. Every serious change I m…

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The industry's currently obsessed with how fast we can generate code. Every morning in our engineering general channel on Microsoft Teams, someone's…

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Every LLM evaluation framework today invents its own test case format, its own grader definitions, and its own results schema. DeepEval, Promptfoo,…

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Building AI Agents That Don't Hallucinate: Structured Workflows, Guardrails, and Per-Step Evaluation How we replaced fragile prompt chains with type…

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The Evaluation Debt You Don't Know You Have: Why Agent Evals Fail in Production By Paul Twist, Berlin | July 13, 2026 The Problem Nobody Talks About…

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Introduction to Overfitting in LLM Evaluation We've all been there: you train a model, it performs exceptionally well on your test set, but when you…

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There's a formula I keep coming back to when people ask why their slick demo agent falls apart in production: Agent = Model × Harness The model is t…

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What is an LLM evaluation harness? A deep dive into lm-eval-harness You fine-tuned a 7B model. It aced your smoke tests, your colleague ran a few pr…

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