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Language models like GPT-4 have established themselves as the leading standard in the NLP industry for developing advanced applications. These models can perform diverse tasks and easily adapt to new tasks using Prompt Engineering Techniques. However, they also present a massive challenge around training and fine-tuning. Due to their massive
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Large language models (LLMs), such as GPT-3 and LaMDA, are at the forefront of natural language processing (NLP). Trained on terabytes of text data, they generate human-like text and power multiple applications, including chatbots, virtual assistants, and search engines. This initial training process, where the LLM learns general language representations
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Retrieval-augmented generation (RAG) is a significant advancement in the ability of large language models (LLMs) to utilize external knowledge for more accurate and contextually rich responses. However, in standard RAG systems, small text chunks are used to isolate precise information for retrieval. This approach works well for focused facts but
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The Retrieval Augmented Generation (RAG) technique enhances the capabilities of large language models (LLMs) by grounding their responses in external knowledge sources. This approach improves the accuracy and relevance of LLM outputs, especially when dealing with domain-specific topics. Traditional RAG systems retrieve a single document to inform responses. While effective
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Introduction In the quickly changing technology landscape of today, artificial intelligence (AI) is a game-changer for companies in a wide range of industries. But what is the hidden energy source for these clever systems? The foundation of contemporary AI applications, AI infrastructure, holds the key to the solution. What is
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AI agents like ChatGPT are advanced systems that operate independently, making decisions and taking actions without needing human intervention. They are becoming increasingly important in today's technology-driven world, as they have the potential to revolutionize various industries. These agents are capable of learning, adapting, and improving over time,
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Chain of Thought Prompting (CoT) - Overview An artificial intelligence method called Chain of Thought prompting encourages sequential thinking, which enables models to handle challenging tasks more effectively. CoT prompting, which mimics human-like problem-solving, allows AI systems to break down difficulties into smaller, more manageable portions. This organized method enhances
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Introduction Technology is constantly changing the way we interact with it, and one of the most exciting areas of progress is artificial intelligence (AI). Human-like thought, learning, and communication are increasingly being taught to machines. Among these developments, prompt engineering is a particular ability that is becoming more and more
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Introduction AI agent frameworks are advanced tools that developers use to build intelligent systems capable of autonomous decision-making and task execution. These frameworks serve as a foundation, allowing developers to focus on high-level system behavior rather than manually coding every aspect of the agents. By using these frameworks, developers can
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In the rapidly evolving world of AI, Large Language Models (LLMs) have become increasingly powerful. However, their true potential is unlocked when they can access and integrate information from multiple data sources. This article will guide you through the process of integrating various data sources to enhance LLM retrieval, making
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Large Language Models (LLMs) have revolutionized natural language processing, but their true value lies in their ability to perform in real-world situations. Testing and validating LLMs with realistic scenarios is crucial for ensuring their reliability, safety, and effectiveness. This article will guide you through the process of rigorously evaluating LLMs
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Large Language Models (LLMs) like GPT-4 and BERT have become essential tools for natural language processing (NLP) tasks. However, deploying these models in real-time applications, such as chatbots or voice assistants, presents unique challenges. Achieving low-latency inference while maintaining high accuracy is critical. This article will guide you through the