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Have we all been tricked into believing in an impossible, extremely expensive future?
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The third and final part for evaluating the retrieval quality of your RAG pipeline with…
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How to Evaluate Retrieval Quality in RAG Pipelines (part 2): Mean Reciprocal Rank (MRR) and Average Precision (AP)
Large Language ModelsEvaluating the retrieval quality of your RAG pipeline with binary, order-aware measures
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Water Cooler Small Talk, Ep. 9: What “Thinking” and “Reasoning” Really Mean in AI and LLMs
Artificial IntelligenceUnderstanding how AI models “reason” and why it’s not what humans do when we think
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How to Evaluate Retrieval Quality in RAG Pipelines: Precision@k, Recall@k, and F1@k
Large Language ModelsIn my previous posts, I have walked you through putting together a very basic RAG…
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How reranking improves retrieval-augmented generation by surfacing the most relevant results
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Let’s take a closer look at how the retrieval mechanism works
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Water cooler small talk is a special kind of small talk, typically observed in office…
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Hitchhiker’s Guide to RAG: From Tiny Files to Tolstoy with OpenAI’s API and LangChain
Large Language ModelsScaling a simple RAG pipeline from simple notes to full books
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Build a simple Python RAG pipeline using your local files as context
7 min read
