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DSPy is a declarative, self-improving framework for LLMs designed to streamline pipelines and aid broader LLM application development processes. The framework allows developers to define tasks and workflows in a high-level, declarative manner using their API. This process simplifies the need to specify “what needs to be done?” rather than

With the rapid adoption of AI agents and their workflows by enterprises and individuals alike in their daily lives, enabling agents to perform complex, long-horizon tasks with complex trajectories has become a significant challenge. The recent paper, “Agent Workflow Memory” introduces a novel approach to address this challenge by equipping

Through the world's Llama Guards and Guardrails AI, one problem persists in modern LLMs: the threat of being jailbroken. Jailbreaking in LLMs has become a growing concern as these models continue to be adapted as AI Assistants and Agents in every single pipeline imaginable. AI adoption has slowly

Introduction In this blog, we’ll explore a research paper that tackles the challenge of grounding LLM outputs in evidence, which is crucial for tasks like retrieval-augmented generation and document-grounded dialogue. The paper introduces a cost-effective approach by creating smaller models that deliver GPT-4-level performance at a fraction of the

Introduction This blog explores Microsoft's Graph-based Retrieval-Augmented Generation (Graph RAG) approach. While traditional RAG excels at retrieving specific information, it struggles with global queries, like identifying key themes in a dataset, which require query-focused summarization (QFS). Graph RAG combines the strengths of RAG and QFS by using entity

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