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Introduction LLMs excel in knowledge-intensive tasks but often struggle with niche or long-tail queries. RAG enhances LLMs by incorporating external knowledge, yet it faces a key challenge: imperfect retrieval. This occurs when retrieved information is incorrect, irrelevant, or conflicting, leading to unreliable results. In this blog, we explore an advanced

Introduction In this blog, we explore the paper on Source2Synth, a framework for synthetic data generation. Source2Synth addresses the common issues of low-quality synthetic data by grounding it in real-world sources and employing a multi-stage curation process. This approach enhances data quality and relevance. What is the problem with synthetic

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