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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
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Retrieval-Augmented Generation (RAG) is a powerful technique that enhances large language models (LLMs) by combining their generative capabilities with the ability to retrieve relevant external information. 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