Retrieval Augmented Generation - RAG Fine Tuning Explained

Learn Retrieval Augmented Generation (RAG) Fine-Tuning and LLM Optimization to Build Accurate Real-World AI Applications

Sub Category

• Data Science

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Objectives

• Understand the fundamentals of Retrieval Augmented Generation (RAG) and how it enhances the performance of Large Language Models (LLMs).
• Learn how to fine-tune LLMs to align with domain-specific tasks and improve accuracy, relevance, and reliability.
• Gain hands-on knowledge of how to implement RAG workflows to connect LLMs with real-time, grounded data sources.
• Explore real-world scenarios and use cases where RAG and fine-tuning empower AI to deliver precise, actionable results in enterprise environments.
• Develop the skills to create custom datasets for fine-tuning and train AI models to adapt to specific organizational needs.
• Master techniques to reduce AI hallucination and ensure AI-generated responses are grounded in facts and context.
• Understand how to combine RAG with fine-tuning (RAFT) to create cutting-edge, domain-specific AI solutions.
• Discover the inner workings of LLMs – Understand how large language models generate responses using probabilistic methods and why this can lead to hallucination
• Learn the importance of context in AI interactions – Explore how providing detailed prompts and context enhances LLM accuracy and relevance.
• Understand embeddings and vector databases – Gain insights into how embeddings help AI interpret queries and retrieve relevant information efficiently.
• Explore knowledge graphs – See how knowledge graphs reduce ambiguity, enhancing AI’s ability to understand relationships between concepts for more accurate resp
• Implement RAFT (Retrieval-Augmented Fine-Tuning) – Master the combination of RAG and fine-tuning to develop AI systems that can retrieve data and respond accura
• Recognize enterprise use cases for RAG and fine-tuning – Learn how companies use RAG to power AI chatbots, virtual assistants, and customer service tools that a
• Design AI solutions that scale – Understand how to implement RAG systems across large organizations, ensuring AI assistants remain up-to-date with evolving data

Pre Requisites

1. Basic understanding of AI and machine learning concepts – Familiarity with how AI models work will help, but is not required.
2. Interest in Large Language Models (LLMs) – A curiosity about how models like GPT function and can be improved.
3. No advanced programming experience required – This course focuses on concepts, workflows, and real-world applications. Technical details are explained in an accessible way.
4. Optional: Familiarity with Python or AI frameworks can enhance your learning experience, but the course covers essential topics without heavy coding.

FAQ

• Q. How long do I have access to the course materials?
  • A. You can view and review the lecture materials indefinitely, like an on-demand channel.
• Q. Can I take my courses with me wherever I go?
  • A. Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don't have an internet connection, some instructors also let their students download course lectures. That's up to the instructor though, so make sure you get on their good side!

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