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TL;DR RAG systems often fail not because of poor embeddings or weak LLMs, but because they feed irrelevant information to the generation stage. Initial retrieval casts a wide net, returning documents that are semantically similar but not actually relevant to […]

Chunking is only one part of the pipeline; the technical RAG component guide shows how retrieval, augmentation, and generation fit together. TL;DR Document chunking is the foundation of every RAG system, yet it’s often treated as an afterthought. The wrong […]

TL;DR When building Retrieval-Augmented Generation (RAG) systems, your vector database choice fundamentally determines performance, cost, and operational complexity. Unlike traditional databases focused on exact matches, vector databases power semantic search by storing high-dimensional embeddings that capture meaning, enabling RAG systems […]

TL;DR Building a RAG system is straightforward—building one that consistently delivers accurate, relevant responses is not. Without proper evaluation metrics, you’re flying blind, unable to distinguish between minor improvements and system-breaking regressions. Traditional NLP metrics like BLEU and ROUGE miss […]