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AAAwais Ahmadblog.itsahmadawais.comJul 28 · 8 min read

LLM Evaluation for Engineers: Building Reliable AI Applications

When building traditional software, engineers write tests to make sure code works correctly. Every time you change a feature or update a package, automated tests run to catch bugs before they reach re

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MPMEET PATELmeetp2022.hashnode.devJul 18 · 8 min read

My RAG Assistant Was Lying to Me About Having a Memory

I built an AI-Governed Enterprise Knowledge Assistant for an internal Ideathon. It's a RAG chatbot that answers questions using only approved cloud infrastructure docs, cites its sources, and shows a

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MPMEET PATELmeetp2022.hashnode.devJun 7 · 8 min read

My RAGAS Scores Varied Between Runs. That Was the Most Useful Finding of All

I have a document assistant that answers questions from enterprise cloud documentation. Azure OpenAI generates the answers, Azure AI Search handles retrieval. It works. People use it. The answers soun

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SSapotaCorpsapotacorpvn.hashnode.devMay 24 · 7 min read

How to answer the CFO question: 'is the AI accurate?'

A founder we work with forwarded an email from his CFO three days before a board meeting. Paraphrased: "before we approve the additional AI budget, I need engineering to give us a number for how accur

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ADAnia Danilecanna-danilec.hashnode.devMay 18 · 14 min read

RAG Evaluation with RAGAS: Measuring Faithfulness, Context Precision, and Recall in Production

Key takeaways: RAGAS gives you four core metrics that split RAG failures into retrieval vs. generation problems Faithfulness catches hallucinations; Context Recall catches retrieval gaps Most metri

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AAAbstract Algorithmsabstractalgorithms.hashnode.devMar 29 · 16 min read

LLM Evaluation Frameworks: How to Measure Model Quality (RAGAS, DeepEval, TruLens)

TLDR: 📏 Traditional ML metrics (accuracy, F1) fail for LLMs because there's no single "correct" answer. RAGAS measures RAG pipeline quality with faithfulness, answer relevance, and context precision.

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AAniblog.anirudha.devNov 16, 2025 · 14 min read

Building AI Tools You Can Trust

You want to build an AI application. Something useful. Something your users can rely on. But here's the problem: How do you know it's actually good? You can build an app that generates summaries, answers questions, or writes emails. It works. Your te...

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AAniblog.anirudha.devNov 9, 2025 · 10 min read

Teaching AI to Grade Other AI

If you’ve been following the world of AI development, you might’ve heard the phrase “LLM-as-Judge.”It sounds dramatic, like some sci-fi overlord where one AI passes judgment on another. But it’s actually one of the most important evolutions in evalua...

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AAniblog.anirudha.devOct 26, 2025 · 5 min read

Unit Tests for Intelligence

A few months ago, as I was exploring machine learning while working on a project, one of my models kept behaving in a weird way. I had built a classifier to detect cats in images. During training, accuracy was awesome, near 99%. But in production, it...

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DRDEEPESH RANJAN KHATRIagents-and-beyond.hashnode.devOct 2, 2025 · 6 min read

Showcasing CrewAI and Ragas: Building SEO Report Automation with Generative AI Agents

A few months back, I had the chance to present a talk at an AI meetup. My topic was Generative AI Agents, and to make the session more practical, I prepared a small hands-on demo using CrewAI and Ragas. The demo was simple but effective:👉 Generate S...

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#ragas

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LLM Evaluation for Engineers: Building Reliable AI Applications

My RAG Assistant Was Lying to Me About Having a Memory

My RAGAS Scores Varied Between Runs. That Was the Most Useful Finding of All

How to answer the CFO question: 'is the AI accurate?'

RAG Evaluation with RAGAS: Measuring Faithfulness, Context Precision, and Recall in Production

LLM Evaluation Frameworks: How to Measure Model Quality (RAGAS, DeepEval, TruLens)

Building AI Tools You Can Trust

Teaching AI to Grade Other AI

Unit Tests for Intelligence

Showcasing CrewAI and Ragas: Building SEO Report Automation with Generative AI Agents

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