Generative AI & RAG Development

Introduction

Artificial Intelligence is transforming how software is built, how businesses operate, and how people interact with technology. Among the most impactful advancements in recent years are Generative AI and Retrieval-Augmented Generation (RAG).

From AI-powered chatbots and coding assistants to enterprise knowledge systems and intelligent search platforms, organizations across industries are rapidly adopting these technologies to improve productivity, automate workflows, and unlock the value of their data.

This learning series has been designed to provide a complete beginner-to-advanced roadmap for students, software professionals, AI engineers, and solution architects who want to build practical expertise in Generative AI and RAG development.

Whether you are preparing for placements, exploring AI as a career path, or building production-ready AI applications, this series will help you understand both the theoretical foundations and practical implementation techniques used in modern AI systems.

Who Should Learn This Series?

This series is designed for:

  • B.Tech (CSE/IT) Students

  • MCA Students

  • M.Tech Students

  • Software Engineers

  • AI Developers

  • Data Engineers

  • Solution Architects

  • Technology Enthusiasts

Prerequisites

To get the maximum benefit from this series, learners should have:

  • Basic Python programming knowledge

  • Basic understanding of Artificial Intelligence concepts

  • Familiarity with APIs and software development

  • Curiosity to explore modern AI technologies

No prior RAG experience is required.

What You Will Learn

By the end of this series, you will be able to:

  • Understand the fundamentals of Generative AI

  • Work confidently with Large Language Models (LLMs)

  • Design effective prompts and instructions

  • Build AI-powered applications

  • Understand embeddings and vector databases

  • Implement Retrieval-Augmented Generation (RAG)

  • Create document question-answering systems

  • Develop enterprise knowledge assistants

  • Implement advanced retrieval strategies

  • Evaluate AI application performance

  • Deploy and monitor production RAG systems

Why Generative AI Matters

Traditional software follows predefined rules written by developers.

Generative AI introduces a different approach. Instead of explicitly programming every behavior, developers provide data, context, and instructions, allowing AI systems to generate responses, summaries, code, insights, and recommendations.

Today, Generative AI is being used in:

  • Customer support automation

  • Content creation

  • Software development assistance

  • Enterprise knowledge management

  • Research and analysis

  • Education platforms

  • Healthcare solutions

  • Financial services

As adoption continues to grow, professionals with practical Generative AI and RAG skills are becoming highly valuable across industries.

Learning Roadmap

Module 1: Generative AI Foundations

  1. Introduction to Generative AI

  2. Evolution of AI: From Machine Learning to Generative AI

  3. Understanding Large Language Models (LLMs)

  4. How Transformers Work

  5. Tokens, Context Windows, and Embeddings

  6. Prompt Engineering Fundamentals

Module 2: Working with LLMs

  1. OpenAI, Gemini, Claude, and Open-Source Models

  2. Temperature, Top-P, and Model Parameters

  3. System Prompts and Instruction Design

  4. Structured Outputs and JSON Responses

  5. Function Calling and Tool Usage

  6. Building Your First AI Application

Module 3: Understanding RAG

  1. What is Retrieval-Augmented Generation (RAG)?

  2. Why LLMs Hallucinate

  3. How RAG Solves Knowledge Limitations

  4. RAG Architecture Explained

  5. Data Ingestion Pipeline

  6. Chunking Strategies

Module 4: Embeddings and Vector Databases

  1. Understanding Embeddings

  2. Creating Embeddings Using Modern Models

  3. Vector Similarity Search

  4. Introduction to Vector Databases

  5. Working with ChromaDB

  6. Working with Pinecone

  7. Working with Weaviate

  8. Comparing Vector Databases

Module 5: Building RAG Systems

  1. Building a Simple RAG Application

  2. PDF Question Answering System

  3. Website Content Chatbot

  4. Enterprise Knowledge Assistant

  5. Multi-Document Retrieval

  6. Metadata Filtering

Module 6: Advanced RAG

  1. Hybrid Search (Vector + Keyword Search)

  2. Re-Ranking Techniques

  3. Context Compression

  4. Query Transformation

  5. Multi-Step Retrieval

  6. Graph RAG Fundamentals

Module 7: Production RAG

  1. Evaluating RAG Applications

  2. Deploying and Monitoring Production RAG Systems

Learning Approach

Each session follows a structured format:

  • Learning Objectives

  • Why This Topic Matters

  • Core Concepts

  • Architecture Diagrams

  • Real-World Examples

  • Python Perspective

  • .NET Perspective

  • Interview Questions

  • Assignments

  • Key Takeaways

  • What's Next?

This approach ensures both academic understanding and industry readiness.

Career Opportunities

After completing this series, learners can pursue roles such as:

  • AI Engineer

  • Generative AI Developer

  • RAG Engineer

  • LLM Application Developer

  • AI Solutions Architect

  • Machine Learning Engineer

  • Conversational AI Developer

  • Knowledge Systems Engineer

Expected Outcome

Upon completing all 40 sessions, learners will possess the practical knowledge required to design, build, evaluate, and deploy modern Generative AI and RAG applications.

The goal is not only to understand the technology but also to develop the confidence to implement real-world AI solutions used by organizations today.

Let's Begin

The journey starts...