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Tag: RAG

  • RAG Best Practices: How to Make AI Smarter, Faster, and More Accurate

    Retrieval-Augmented Generation (RAG) techniques have emerged as a pivotal advancement in enhancing large language models (LLMs), enabling them to access up-to-date information, reduce hallucinations, and improve response quality, especially in specialized domains. A recent study titled “Searching for Best Practices in Retrieval-Augmented Generation” delves into optimizing RAG workflows to balance performance and efficiency. export.arxiv.org Understanding…

  • Retrieval-Augmented Generation: Enhancing LLMs with External Knowledge

    Large Language Models (LLMs) have revolutionized natural language processing, demonstrating remarkable capabilities across various tasks. However, they face challenges such as hallucinations—generating incorrect or nonsensical information—outdated knowledge, and opaque reasoning processes. To address these issues, Retrieval-Augmented Generation (RAG) has emerged as a promising approach by integrating external knowledge sources into the generation process. Understanding Retrieval-Augmented…

  • Optimizing AI Retrieval: The Science Behind Effective Chunking

    In the realm of AI applications, document chunking is a pivotal pre-processing step that divides extensive texts into manageable units for efficient retrieval and processing by large language models (LLMs). Despite its widespread use, the impact of different chunking strategies on retrieval performance has not been thoroughly examined. Chroma Research’s technical report, “Evaluating Chunking Strategies…

  • Chunking Strategies for LLM Applications: A Comprehensive Guide

    In the rapidly evolving landscape of Large Language Models (LLMs), one technique stands out as a cornerstone for building efficient applications: chunking. This fundamental process involves breaking down larger texts into smaller, manageable segments, a strategy that is crucial in enhancing both the accuracy and efficiency of content retrieval from a vector database when leveraging…

  • Solving Context Loss in AI: The Power of Contextual Retrieval

    In September 2024, Anthropic introduced a groundbreaking approach to enhance Retrieval-Augmented Generation (RAG) systems: Contextual Retrieval. This innovative method addresses a common challenge in traditional RAG systems—loss of context during information retrieval—by incorporating two key techniques: Contextual Embeddings and Contextual BM25. Understanding the Challenge in Traditional RAG Systems RAG systems are designed to improve AI…