In this video, we dive into Retrieval-Augmented Generation (RAG), a powerful method for enhancing AI with access to external data sources. We’ll explore how RAG works, why it’s valuable, and when to use it compared to fine-tuning models. You’ll gain a clear understanding of RAG’s benefits, how it complements large language models, and when it’s best to use both RAG and fine-tuning for robust AI solutions. Perfect for AI enthusiasts, tech professionals, or anyone curious about advanced AI applications!

Key Points Covered:

• What is Retrieval-Augmented Generation (RAG)?

• How RAG systems work with external data

• The differences between RAG and fine-tuning

• Examples of RAG applications in business and AI workflows

• When to combine RAG with fine-tuning for the best results

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In this episode of The Tech Journey, we dive into the fascinating world of large language models (LLMs) and uncover how they generate the text we see in apps, chatbots, and more.

Ever wondered how AI seems to know what to say?

We’ll explore the step-by-step process, from embeddings and context windows to self-attention, breaking down the technology in a way that’s easy to understand. You’ll discover why some AI models sound more relevant than others, how they adapt to different topics, and why their responses vary each time.

If you’re curious about the tech that powers AI, don’t miss this episode!

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In this episode, we dive into the fascinating world of AI alignment—the techniques used to make AI systems not only powerful but safe and trustworthy. We’ll explore how methods like Reinforcement Learning from Human Feedback (RLHF) and content filtering help align AI models with human values and ethics, allowing them to assist us in everything from customer support to learning new skills. But with powerful open-source models also comes the risk of misuse, especially when they lack alignment.

Join me as we uncover both the positive impact of aligned models and the challenges posed by unfiltered, unaligned models. It’s a balanced look at how we can harness AI’s potential responsibly in an ever-evolving technological landscape.

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