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The legal and judicial landscape has long relied on historical data, expert opinions, and case precedents to determine case outcomes. However, with the advent of Artificial Intelligence (AI)-powered predictive analytics, the approach to legal decision-making is undergoing a seismic shift. Predictive analytics leverages machine learning (ML) models, natural language
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From creating code snippets to debugging and program optimisation, Large Language Models (LLMs) have made amazing progress helping with software development. However, to evaluate their practical value, assessing these models calls for thorough benchmarking. Several benchmarks have been created to evaluate an LLM's capacity to manage programming assignments,
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AI-driven code generation has transformed the way developers write, debug, and optimize software. With open-source models becoming more powerful and accessible, developers no longer have to rely on proprietary solutions like GitHub Copilot or OpenAI’s GPT-4. In this article, we’ll explore Top 5 Open-Source Coding Models with less
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Introduction In our Advanced RAG Techniques series, today we’re exploring a powerful technique to improve traditional RAG called Corrective RAG (CRAG). Since LLMs have the limitation of generating responses based on their pre-trained knowledge, RAG has become a popular technique for making them smarter by connecting them to external
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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 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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In 2023, Retrieval-Augmented Generation (RAG) became one of the most talked-about breakthroughs in AI, transforming how systems handle context and knowledge. The 2024 was the year of AI Agents, which opened up exciting possibilities for building more autonomous and interactive applications. If you’re just entering the world of Generative
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Large Language Models (LLMs) have transformed artificial intelligence by understanding and generating human-like text. For AI development teams—including engineers, researchers, data scientists, and product developers—accurately assessing these models is vital. Benchmarking evaluations offer insights into the strengths and weaknesses of different LLMs, guiding further research and development. Drawing
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As AI development teams increasingly leverage pre-trained language models (PLMs) for diverse applications, understanding the nuances of model adaptation techniques becomes crucial. This article provides an in-depth technical comparison of fine-tuning and prompt tuning, specifically tailored for AI engineers, researchers, data scientists, and product developers seeking to optimize PLM performance
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Retrieval Augmented Generation (RAG) represents a significant advancement in the field of natural language processing (NLP), merging the strengths of retrieval-based systems and generative models. This hybrid approach not only enhances the accuracy and relevance of generated responses but also ensures that the information remains current without the need for
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Organizations increasingly deploy Large Language Models (LLMs) across applications, making effective evaluation crucial. While these models excel at language processing, assessing their performance poses unique challenges that traditional methods struggle to address. Language evaluation of LLMs faces three critical challenges. First, while LLMs can generate grammatically flawless text, they may
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Retrieval Augmented Generation (RAG) systems are the backbone of an AI pipeline as they augment LLMs with external knowledge. This ensures that AI responses are both precise and contextually relevant by grounding them in factual data. This guide provides actionable insights for AI development teams, helping you build robust, scalable,