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Small Language Models, often known as SLMs, have become increasingly popular as a result of their effectiveness and accessibility. Opposite to their larger counterparts, SLMs are intended to carry out particular tasks with a limited amount of computational resources. This makes them an excellent choice for a wide range of
Large Language Models (LLMs) are revolutionizing industries with their ability to answer questions, generate content, and write code. But they often struggle with producing consistent, structured outputs. Ask for JSON, and you might get a mix of text and JSON—sometimes spot on, sometimes a mess. This makes them hard
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Evaluating Large Language Models (LLMs) is crucial for ensuring their effectiveness in applications like chatbots, document summarization, and retrieval-augmented generation (RAG). Open-source frameworks simplify this process by providing tools to assess various aspects of LLM performance. In this article, we explore the top 6 open source LLM evaluation frameworks with
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As January unfolds, the excitement in Artificial Intelligence remains at an all-time high, with Large Language Models (LLMs) and AI agents driving transformative advancements. This week, a new wave of groundbreaking research emerges, focusing on AI Agents, LLM Benchmarking, and Retrieval-Augmented Generation (RAG). In this article, we highlight 10 Cutting-Edge
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As LLMs continue to revolutionize industries, their role in powering Retrieval-Augmented Generation (RAG) systems has gained significant attention. A key challenge in RAG workflows is the efficient and accurate ingestion of diverse data sources into LLMs, a process that directly impacts the system’s performance and reliability. Developers and researchers
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As we move into the second week of the New Year, the excitement in Artificial Intelligence remains electrifying, with Large Language Models (LLMs) continuing to dominate the forefront of innovation. This week, another wave of groundbreaking research has emerged, further expanding the horizons of what LLMs can achieve and tackling
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This year, AI agents levelled up—moving beyond simple tools to become intelligent systems that can reason, collaborate and tackle complex tasks. Unlike traditional AI models constrained by static parameters, these agents integrate decision-making and execution, operating in decentralised or multi-agent ecosystems to tackle real-world challenges across robotics, healthcare, and
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As we step into the first week of the New Year, the momentum in Artificial Intelligence continues to surge, with Large Language Models (LLMs) leading the charge. This week, a wave of groundbreaking research has surfaced, pushing the boundaries of what LLMs can accomplish and addressing key challenges in their
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This year, Retrieval-Augmented Generation (RAG) has been one of the hottest topics in tech, reshaping how we think about Enterprise AI. For this rapid development, Hacker News emerged as a hub for critical discussions with insights, breakthroughs, and debates about RAG throughout 2024. In this post, we’ve compiled the
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The development of LLMs has sparked a revolution in Artificial Intelligence, driving innovation across every industry. Recent research pushed the boundaries of what these models can achieve, addresses their limitations, and opens new horizons for their application. Let’s dive into the Top 10 Research Papers from past week that
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In this blog, we’ll explore how DoorDash leverages Retrieval-Augmented Generation (RAG) to address Dashers’ queries using insights from the company’s internal blogs. We’ll conduct practical experiments based on the techniques discussed in DoorDash’s Tech blog, utilizing a curated DoorDash dataset. We will use Athina AI which
