Best of RAG — November 2024

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   Article

   Daily Dose of Data Science | Avi Chawla | Substack·1y

   A Crash Course on Building RAG Systems – Part 4

   Part 4 of the crash course on building RAG systems focuses on implementing RAG on multimodal data, specifically complex documents with tables, texts, and images. This series covers foundational components, evaluation methods, optimization techniques, and handling large data sets, making it highly beginner-friendly. Understanding how to build reliable RAG systems can reduce costs and enhance scalability for enterprises, bypassing the need for fine-tuning large language models (LLMs).

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2. 2
   Article

   Weaviate·2y

   What is Agentic RAG

   Agentic RAG is an advanced AI framework enhancing the traditional Retrieval-Augmented Generation (RAG) pipelines by incorporating AI agents. These agents possess memory, planning, and tool capabilities to perform various actions beyond simple information retrieval. The architecture can range from single-agent systems acting as routers to complex multi-agent setups coordinating multiple specialists. This approach addresses the limitations of vanilla RAG by providing tools, multi-step retrieval, and validation, thereby improving response accuracy and robustness, while introducing potential latency and reliability issues inherent to LLMs.

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3. 3
   Article

   GoPenAI·2y

   Refining RAG Accuracy with TrueLens: An Evaluation Guide

   In today's AI landscape, Retrieval-Augmented Generation (RAG) significantly enhances Large Language Models (LLMs) by leveraging user-specific data for context-driven responses. To ensure quality, rigorous evaluation frameworks like TruLens are essential. This guide explores the use of TruLens's feedback functions to assess context relevance, groundedness, and answer relevance, helping to improve RAG pipelines by minimizing risks such as hallucinations and biases. The step-by-step instructions illustrate how to set up and evaluate a RAG pipeline, ensuring consistency and high performance in AI-driven responses.

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   Article

   Daily Dose of Data Science | Avi Chawla | Substack·1y

   Traditional RAG vs. HyDE

   Traditional RAG systems often retrieve irrelevant contexts due to questions not being semantically similar to their answers. HyDE mitigates this by generating a hypothetical answer to the query and embedding it using a contriever model to fetch more relevant contexts. While this improves retrieval performance, it comes with increased latency and more LLM usage.

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