Udemy - Retrieval Augmented Generation - RAG Fine Tuning Explained

Retrieval Augmented Generation - RAG Fine Tuning Explained

https://DevCourseWeb.com

Published 12/2024
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Language: English | Duration: 32m | Size: 387 MB

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

What you'll learn
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

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