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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,
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Parameter-efficient fine-tuning (PEFT) methods address the challenge of fine-tuning large language models (LLMs) for specific downstream tasks when the cost of training all model parameters becomes prohibitive. Fine-tuning large language models with billions of parameters can be computationally expensive, require significant storage, and lead to overfitting, especially when adapting to
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Why Tools Define Success for AI Developers AI continues to evolve at a breakneck pace, with new breakthroughs and methodologies emerging almost daily. For AI developers, keeping up with these advancements isn’t just about staying relevant. It’s also about gaining a competitive edge. The right tools can mean
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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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In our previous articles, we've discussed the importance of RAG and how it connects LLMs to external data sources. This approach improves the accuracy and relevance of LLM outputs, especially when dealing with domain-specific topics. Traditional RAG systems retrieve k-documents for a query and use them to construct
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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