Mastering Generative AI: From Basics to Advanced Applications

Journey Through LLMs, Retrieval-Augmented Generation, and Agentic AI

Duration

3 Days (8 hours per day)

Level

Advanced Level

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This intensive 3-day course aims to equip participants with a thorough understanding of Generative AI, focusing predominantly on open-source Large Language Models (LLMs). Participants will explore the foundational concepts, practical applications, and advanced techniques in Generative AI, including Retrieval-Augmented Generation (RAG) and Agentic AI, all without relying on cloud deployment. The course combines theoretical insights with hands-on sessions to ensure a comprehensive learning experience.

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Day 1: Introduction to Generative AI and Open-Source LLMs

Overview of Generative AI
- What is Generative AI?
- Significance and Use Cases of Generative AI
- Historical context and evolution of systems – Traditional vs ML based vs GenAI vs Agentic AI
- Applications across various industries
Introduction to Large Language Models (LLMs)
- What is LLM?
- What are Language Models?
- What is “Large” in LLMs?
- Understanding LLM Architecture
- Differences between open-source and proprietary LLMs
- Ethical considerations in LLM deployment

Exploring Open-Source LLMs
- Survey of popular open-source LLMs (e.g., LLaMA, Falcon, Mistral)
- Understanding Architectures of LLaMA, Falcon, Mistral, etc.
- Installation and setup of open-source LLMs locally
- Hands-on session: Running a simple open-source LLM
Data Preparation for LLMs
- Sourcing and curating datasets for training LLMs
- Data cleaning and preprocessing techniques
- Hands-on session: Preparing a dataset for LLM training

Day 2: Advanced Techniques in Open-Source LLMs

Fine-Tuning Open-Source LLMs
- Understanding the fine-tuning process
- Factors for fine-tuning
- Parameter-efficient fine-tuning methods
- Hands-on session: Fine-tuning an open-source LLM for a specific task
Evaluation and Optimization
- Metrics for assessing LLM performance
- Techniques for optimizing LLM efficiency
- Hands-on session: Evaluating and optimizing a fine-tuned LLM

Introduction to Retrieval-Augmented Generation (RAG)
- Concept and significance of RAG
- Understanding RAG implementations
- Various Available Tools for RAG Implementation
- Integrating retrieval mechanisms with LLMs
- Hands-on session: Implementing a simple RAG system
Exploring Agentic AI
- Understanding Agentic AI and its applications
- Current trends and future directions
- Case studies of Agentic AI implementations

Day 3: Practical Applications and Deployment

Building Applications with Open-Source LLMs
- Designing AI applications without cloud dependencies
- Tools and frameworks for local deployment
- Hands-on session: Developing a simple AI application using an open-source LLM
Ethical and Legal Considerations
- Understanding the ethical implications of AI applications
- Compliance with legal standards and regulations
- Best practices for responsible AI development

Capstone Project
- Participants will work in groups to design and implement a mini-project that incorporates Generative AI, open-source LLMs, Agentic AI and RAG techniques

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Mastering Generative AI: From Basics to Advanced Applications

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