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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