Retrieval-Augmented Generation (RAG) fails most often not because of the Large Language Model (LLM), but because of poor data preparation. When you feed a vector database large, disorganized blocks …

Relying solely on dense vector search often causes Retrieval-Augmented Generation (RAG) systems to fail when users search for exact technical terms, product IDs, or specific acronyms. While embeddi…

Feeding monolithic PDFs into Large Language Models (LLMs) destroys context accuracy and causes massive hallucination rates. In an enterprise environment, where precision is non-negotiable, your Ret…