Latest

Introduction In this blog, we explore the RAGChecker framework for its effectiveness in evaluating Retrieval-Augmented Generation (RAG) systems. RAGChecker addresses significant challenges in RAG evaluation, including the modular complexity of RAG systems, the assessment of long-form responses, and the reliability of measurements. Meta-evaluation has shown that RAGChecker aligns more closely

Introduction Evaluating Retrieval-Augmented Generation (RAG) systems in specialized domains like finance, healthcare, and legal presents unique challenges that existing benchmarks, focused on general question-answering, fail to address. In this blog, we will explore the research paper "RAGEval: Scenario Specific RAG Evaluation Dataset Generation Framework," which introduces RAGEval. RAGEval

Introduction In this blog, we explore the evolving landscape of large language models (LLMs) and their approaches to handling extensive text and complex queries. We delve into the capabilities of long-context LLMs, which excel at managing large volumes of information but come with high computational costs. In contrast, we examine

Introduction In the five-part series "Understanding RAG," we began by explaining the foundational Retrieval-Augmented Generation (RAG ) framework and progressively explored advanced techniques to refine each component. In Part 1, we provided an overview of the RAG framework. Subsequent parts offered an in-depth analysis of the three main components:

Introduction In part one of the "Understanding RAG series," we outlined the three main components of RAG: indexing, retrieval, and generation. In the previous parts, we discussed techniques for improving indexing and retrieval. This blog will focus on methods to enhance the generation component of Retrieval-Augmented Generation (RAG)

Introduction Creating a robust Retrieval Augmented Generation (RAG) application presents numerous challenges. As the complexity of the documents increases, we often encounter a significant decrease in the accuracy of the generated answers. This issue can stem from various factors, such as chunk length, metadata quality, clarity of the document, or

Introduction In part one of the "Understanding RAG" series, we covered the basics and advanced concepts of Retrieval-Augmented Generation (RAG). This part delves deeper into the retrieval component and explores enhancement strategies. In this blog, we will explore techniques to improve retrieval and make pre-retrieval processes, such as

What is a RAG? Retrieval-augmented generation (RAG) is a process designed to enhance the output of a large language model (LLM) by incorporating information from an external, authoritative knowledge base. This approach ensures that the responses generated by the LLM are not solely dependent on the model's training

What is "hallucinations" in LLM? Significant advancements in Natural Language Processing (NLP) have been achieved with the advent of Large Language Models (LLMs). These models excel in generating coherent and contextually relevant text. However, they are prone to "hallucinations." In the context of LLMs, hallucinations refer

What is LongRAG? What's the buzz around this? In this blog post, we are taking references from the LongRAG research paper to provide a detailed explanation of LongRAG. LongRAG (Long Retrieval-Augmented Generation) is a framework that uses long-context language models to enhance traditional Retrieval-Augmented Generation (RAG) methods. In

As LLMs advance, evaluating their quality is increasingly complex. Using a single large model like GPT-4 as a judge is costly and biased. A research paper by the Cohere team suggests using a Panel of LLM evaluators (PoLL) with smaller models for more accurate, unbiased, and cost-effective assessments. What’s