Measuring RAG End to End: Retrieval, Evidence, and Answer QualityIn Part 3 of this series, I explored advanced retrieval techniques such as small-to-large retrieval, sentence windows, summary-to-detail retrieval, and LLM reranking. These methods aim to improve whicSep 27 · 9 min readcurious-pm.hashnode.dev
Beyond Basic Retrieval: Improving Precision and Context in RAGIn Part 2 of this series, I explored the basic retrieval mechanisms used in RAG: dense retrieval, BM25, and hybrid search. I also examined evidence grading and Corrective RAG as ways to prevent factuaAug 2 · 9 min readcurious-pm.hashnode.dev
How RAG Finds the Right Evidence: Dense, Sparse, and Hybrid RetrievalIn the first article of this series, I explored how raw documents become retrievable knowledge. I loaded different file formats, preserved their metadata, and divided their contents into chunks. Once Aug 2 · 10 min readcurious-pm.hashnode.dev
RAG From First Principles: How Documents Become Retrievable KnowledgeThis series continues the journey from my earlier work on understanding language models from the inside out. In my first Hashnode series, beginning with Why I Built a GPT Model From Scratch, I assemblAug 2 · 7 min readcurious-pm.hashnode.dev
Temperature, Top-k, and Top-p: How Decoding Changes What a GPT Model Says TL;DR A GPT model does not directly choose its next word. More precisely, it assigns probabilities to thousands of possible next tokens. Decoding is the step that converts those probabilities into an Jul 22 · 10 min readcurious-pm.hashnode.dev
