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Introduction Businesses require AI systems that not only react quickly but also provide precise, customized responses in the highly competitive landscape of today. Retrieval-Augmented Generation (RAG), a newly developed AI architecture, bridges the gap between pre-trained models and real-time business requirements. Let's explore RAG's definition, operation,
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Introduction In the fast-paced world of artificial intelligence, Large Language Models (LLMs) have transformed how we interact with and leverage AI technologies. However, these powerful models come with challenges, including high costs and resource-intensive operations. Enter LLM distillation – a game-changing technique that enables developers to harness the power of LLMs
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Introduction For the last few years Large Language Models have taken the center stage in the world. It is interesting to see how tech nerds, businesses as well as ordinary people can be excited over the idea of a powerful AI system. But why exactly is LLM and why does
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Fine-tuning large language models (LLMs) is a powerful technique to adapt pre-trained models to specific tasks, such as sentiment analysis, text summarization, or even custom chatbot development. However, the quality of your fine-tuned model heavily depends on the dataset you use. Building a custom dataset tailored to your specific needs
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Introduction In today’s time of rapid advancement in the evolution of artificial intelligence, myths can easily get to people. A very good example of that is the “Mixture of Experts” technique in large language models. As the name suggests, that would be a team of specialized models, but the
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Introduction In the rapidly evolving world of artificial intelligence, speed and accuracy are paramount. Enter semantic caching, a game-changing technology that's revolutionizing how AI applications handle data retrieval and processing. Unlike traditional caching methods, semantic caching brings a new level of intelligence to data storage and retrieval, making
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Introduction As AI and machine learning continue to make rapid strides, developing LLM-powered applications has become both interesting and challenging. In this blog post, we will take you through all of the crucial components and tools that you'll need to build a robust tech stack from scratch for
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Outline 1. Introduction * Importance and benefits of RAG 2. Understanding Retrieval-Augmented Generation * What is RAG? * Key differences from traditional LLMs 3. Steps to Integrate RAG * Choosing the right retrieval mechanism * Implementing the retrieval step * Integrating retrieval with generation 4. Best Practices * Optimizing for large-scale data * Fine-tuning RAG models * Avoiding common
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Outline 1. Introduction * Importance of controlling LLM outputs * Overview of prompt engineering 2. Understanding Prompt Engineering * What is prompt engineering? * Key principles of prompt engineering 3. Techniques for Effective Prompt Engineering * Using explicit instructions * Leveraging examples and few-shot learning * Controlling tone and style 4. Practical Examples * Case studies: From generic
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Outline 1. Introduction * Importance of optimizing LLM inference * Challenges in real-time applications 2. Understanding Inference Bottlenecks * Latency factors * Model size and complexity * Hardware limitations 3. Techniques for Optimizing Inference * Model quantization * Distillation and pruning * Batch processing and caching 4. Infrastructure and Deployment Considerations * Choosing the right hardware (GPUs, TPUs) * Edge
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Introduction In the rapidly evolving world of artificial intelligence, large language models (LLMs) have become the cornerstone of numerous applications. But what happens when you need these models to perform specialized tasks? Enter the world of LLM fine-tuning – a powerful technique that bridges the gap between generic models and specific
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The composition of AI development teams significantly impacts the effectiveness and fairness of AI systems. A diverse team brings a variety of perspectives, enabling them to address challenges more comprehensively. Furthermore, inclusive teams are better equipped to design AI systems that serve a broader range of users effectively. Key Takeaways