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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 […]

TL;DR Document chunking is the foundation of every RAG system, yet it’s often treated as an afterthought. The wrong chunking strategy can cripple your RAG performance regardless of how sophisticated your embedding model or LLM is. Poor chunking leads to […]

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 […]