Generative AI: LLM, Fine-tuning, RAG & Prompt Engineering Explained

Generative AI : LLM, Fine-tuning, RAG & Prompt Engineering

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Last Updated: 2025-04-04 03:04:58 English Course Type: Self Learning

Access and fine-tune over 100 models on Hugging Face, leveraging 100,000+ datasets.
Save 10+ hours per week by automating your prompts Work on a real-world project that covers everything from dataset creation to deployment.
Deploy models easily with Hugging Face using pre-built Gradio templates Configure RAG on Google Gemini Pro.
Master RAG concepts with LangChain to build retrieval-augmented systems.
Build every kind of LLM-based application.
Gain hands-on experience in cost-effective model fine-tuning with open-source tools.
Quickly create new datasets in a few simple steps.
Learn fine-tuning techniques used by industry experts.
Explore advanced techniques like Fusion Retrieval and GraphRAG.
Use free GPU resources and hosting.
Learn how to evaluate your model's performance
Understand how to handle edge cases, biases, and ethical considerations when working with AI models

This course covers everything from Large Language Models (LLMs) and prompt engineering to fine-tuning , as well as advanced concepts like Direct Preference Optimization (DPO). You'll also dive deep into Retrieval-Augmented Generation (RAG), which enhances your LLMs' capabilities by integrating retrieval systems for more accurate and superior responses.

By the end of this course, you'll be equipped to create AI solutions that align perfectly with human intent and outperform standard models.

What You Will Get

In addition to the core topics, our course features in-depth, real-world case studies on fine-tuning, prompt engineering, and Retrieval-Augmented Generation (RAG). These case studies not only highlight cutting-edge techniques but also offer practical, hands-on insights into their application in real-world AI projects. By exploring actual scenarios and projects, learners will gain a deep understanding of how to effectively utilize these methods to solve complex challenges. The case studies are designed to bridge the gap between theory and practice, enabling participants to see how these advanced techniques are deployed in industry settings.

Moreover, these examples provide a step-by-step framework for applying theoretical concepts to real-world applications. Whether it's fine-tuning models for enhanced performance, engineering prompts for improved outputs, or leveraging retrieval systems to augment generation, learners will be able to confidently implement these strategies in their own projects. This ensures that by the end of the course, participants will not only have a solid foundation in generative AI concepts but also the ability to apply them in practical, impactful ways.

A genuine curiosity to learn about Generative AI and its applications.
Familiarity with Python is helpful.

Perfect for those who are curious about AI and want to understand the basics of Generative AI and its real-world applications.
Students or recent graduates looking to build foundational knowledge in AI, LLMs (Large Language Models), and prompt engineering.
For professionals from any field who want to explore how Generative AI can be integrated into business operations and decision-making.
Designed for those looking to understand how AI can enhance business processes and innovation through effective AI tools and how AI can be used to create images, text, and even code.

Section 1 : Welcome 2 Lectures
Lecture 1 :
Unlock AI Mastery: What You’ll Gain From Our Generative AI Course? Preview
Lecture 2 :
Why Generative AI? Our Vision and Purpose for Offering this Course.
Section 2 : Large Language Model 10 Lectures
Lecture 1 :
Introduction
Lecture 2 :
Training Large Language Models
Lecture 3 :
Factors Influencing LLM Performance
Lecture 4 :
LLM Use Cases
Lecture 5 :
Challenges Faced By LLMs Currently
Lecture 6 :
Domain-Specific Fine-Tuning
Lecture 7 :
Domain-Specific Pre-Training Using Bloomberg GPT
Lecture 8 :
When To Use Domain-Specific Pre-Training
Lecture 9 :
Using LLMs Effectively
Lecture 10 :
Benefits of Domain-Specific LLMs
Section 3 : Prompt Engineering 4 Lectures
Lecture 1 :
Introduction
Lecture 2 :
Components of Prompt Engineering
Lecture 3 :
Automating Your Prompts
Lecture 4 :
Live Project on Prompt Engineering
Section 4 : Fine-Tuning 7 Lectures
Lecture 1 :
Introduction
Lecture 2 :
Types of Fine-Tuning for Large Language Models
Lecture 3 :
Instruction Fine-Tuning Technique
Lecture 4 :
Reinforcement Learning from Human Feedback (RLHF)
Lecture 5 :
Direct Preference Optimization (A Modern Approach to Optimize LLMs)
Lecture 6 :
Parameter-Efficient Fine-Tuning (Approach to Tackle High Computational Demands))
Lecture 7 :
Live Project: Fine-Tuning Llama 2 with QLoRA
Section 5 : Advanced RAG and Fine-Tuning Tools 8 Lectures
Lecture 1 :
Introduction
Lecture 2 :
Retrieval Augmented Generation (RAG) in Detail
Lecture 3 :
How Ingestion Works in RAG?
Lecture 4 :
How Retrieval works in RAG?
Lecture 5 :
How Generation works in RAG?
Lecture 6 :
Industry Expert Talk: Introduction to RAG Pipeline
Lecture 7 :
Industry Expert Talk: How RAG Pipeline Works?
Lecture 8 :
Live Project: RAG system using LangChain and Google's Gemini Pro
Section 6 : Evaluation Techniques for Large Language Models 2 Lectures
Lecture 1 :
Evaluation, Building and Deployment of LLMs on Huggingface
Lecture 2 :
Tools for Building LLM Applications
Section 7 : Game Changing Gen AI Strategies for Business Leaders 1 Lectures
Lecture 1 :
Industry Expert Talk: IDEAA

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