Applied Generative AI, RAG & AI Agents
This program equips learners with practical skills to build intelligent AI applications using Generative AI, Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), and AI agents. Through hands-on labs, guided projects, and real-world use cases, participants will learn to develop AI-powered assistants, build knowledge-based chatbots, automate tasks, and deploy AI applications.
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Advanced
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Applied Generative AI, RAG & AI Agents
Develop practical skills in Generative AI, Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), and intelligent AI agents through hands-on learning, coding exercises, and real-world projects.
About the Program
Artificial Intelligence is transforming the way we learn, work, create, and solve problems. The Applied Generative AI, RAG & AI Agents program is designed to equip school and college students with the foundational knowledge and practical skills required to develop intelligent, AI-powered applications.
The program introduces learners to Generative AI, Large Language Models (LLMs), prompt engineering, and Python-based AI application development. It progressively covers Retrieval-Augmented Generation (RAG), embeddings, vector databases, semantic search, and AI agents capable of using tools to perform tasks and execute structured workflows.
Through instructor-led sessions, live demonstrations, guided coding laboratories, assignments, and project-based learning, participants will gain experience in building AI-powered study assistants, document-based question-answering systems, knowledge assistants, and intelligent research tools. Learners will also explore how RAG and AI agents can be combined to develop context-aware applications that retrieve relevant information, use external tools, and generate evidence-based responses.
The program further introduces application testing, evaluation, deployment, data privacy, AI safety, and responsible AI practices. Learners will apply these concepts through practical exercises and mini-projects before developing a capstone project that addresses a real-world problem.
Key Learning Outcomes
Generative AI & LLMs
Understand language models, prompt engineering, and model integration.
AI Application Development
Build AI-powered applications using Python, APIs, and suitable frameworks.
RAG & Knowledge Systems
Create document-based AI assistants using embeddings and retrieval techniques.
AI Agents & Automation
Develop agents that use tools and execute structured, multi-step workflows.
Testing & Deployment
Test, evaluate, and deploy basic AI applications.
Responsible AI
Apply privacy, security, verification, and human-oversight principles.
Who Should Join?
- School students interested in Artificial Intelligence and technology.
- College students seeking practical AI development skills.
- Aspiring AI developers interested in Generative AI, RAG, and AI agents.
- Learners who want to develop practical projects and build an AI portfolio.
Learning Methodology
The program follows a progressive, hands-on learning approach combining conceptual instruction, live demonstrations, guided coding exercises, independent assignments, mini-projects, and capstone development. Learners will progress from foundational concepts to building, testing, and demonstrating functional AI applications.
Capstone Project
As part of the final project, learners will design and develop an AI-powered application using Generative AI, RAG, AI agents, or a combination of these technologies. They will demonstrate their solution, explain their technical decisions, test application behaviour, and document its capabilities and limitations.
Build Your Future with Applied AI
By the end of the program, learners will have a practical understanding of modern AI technologies and experience developing intelligent applications. The program aims to strengthen problem-solving, computational thinking, creativity, and the ability to turn AI concepts into working solutions.
What you'll learn
Requirements
- Open to school students (preferably Classes XI–XII)
- college students
- graduates
- and aspiring AI developers. No prior experience in Generative AI
- RAG
- or AI agents is required. Basic computer literacy and familiarity with using the internet. Basic programming knowledge is helpful but not mandatory. Willingness to learn Python programming and participate in hands-on coding exercises. A laptop or desktop computer with a stable internet connection. Access to a modern web browser and the development tools specified during the course. Some practical exercises may require API access or model usage credits
- depending on the tools selected.
Curriculum
Build a strong programming foundation to develop AI-powered applications using Python.
Topics covered
- Python fundamentals: variables, data types, operators, conditions, and loops
- Functions, lists, dictionaries, strings, and file handling
- Working with JSON, Python libraries, and Jupyter Notebooks
- API integration, HTTP requests, and environment variables
- Debugging, exception handling, and introductory Git workflows
Tools & tech
Python
Jupyter Notebook
Google Colab
Git
REST APIs
JSONWhat you'll build
Python-Based AI Utility Application
Understand how Generative AI works and develop effective applications using Large Language Models (LLMs).
Topics covered
- Generative AI fundamentals, LLM architecture concepts, and tokens
- Prompt engineering, system instructions, and few-shot prompting
- Structured outputs, context management, and prompt optimization
- Integrating LLMs through APIs and exploring open-source models
- Hallucinations, bias, model limitations, and responsible AI practices
- Comparing model outputs and improving response quality
Tools & tech
LLM APIsPythonGoogle Colab
Prompt Engineering
Open-Source ModelsWhat you'll build
AI-Powered Study Assistant for Explanations, Notes & Quizzes
Transform language model capabilities into usable applications with interactive interfaces and reliable functionality.
Topics covered
- Integrating LLMs into Python applications
- Managing conversation history and multi-turn interactions
- Structured data extraction and streaming responses
- API error handling, latency management, and token cost optimization
- Building interactive AI interfaces
- Input validation and application workflow design
Tools & tech
PythonLLM APIs
StreamlitREST APIsJSONJupyter NotebookWhat you'll build
Interactive AI Application with a User-Friendly Interface
Build knowledge-based AI systems that generate answers using information retrieved from documents and trusted knowledge sources.
Topics covered
- RAG architecture and comparison with prompting and fine-tuning
- Document ingestion, parsing, cleaning, and chunking
- Embeddings and semantic similarity
- Vector databases, indexing, and document retrieval
- Context construction and grounded answer generation
- Source citations and handling questions with insufficient evidence
Tools & tech
Python
LangChain
Embedding Models
Vector Databases
PDF LoadersGoogle ColabWhat you'll build
Document-Based AI Knowledge Assistant with Source Citations
Improve the relevance, accuracy, and reliability of RAG applications through advanced retrieval techniques and systematic evaluation.
Topics covered
- Semantic search, keyword search, and hybrid retrieval
- Metadata filtering and contextual retrieval
- Query rewriting and retrieval optimization
- Reranking and relevant context selection
- Evaluating retrieval quality, answer correctness, and faithfulness
- Test datasets, failure analysis, and prompt injection risks
Tools & tech
PythonVector DatabasesLangChain
RAG Evaluation FrameworksEmbedding ModelsWhat you'll build
Advanced RAG System with Evaluated Retrieval and Improved Answer Quality
Develop intelligent AI agents that use tools, process information, and execute structured tasks with appropriate controls.
Topics covered
- AI agents versus conventional chatbots and fixed workflows
- Agent planning, actions, observations, and task decomposition
- Tool calling and function calling
- Agent state, memory, and contextual information
- Connecting agents to search tools, calculators, APIs, and databases
- Managing tool failures, execution limits, and human approval
Tools & tech
PythonLangChainLLM APIs
Function CallingREST APIsWhat you'll build
AI Research Assistant with Tool Calling and Structured Reports
Combine retrieval systems, AI agents, and workflow orchestration to develop intelligent applications capable of completing multi-step tasks.
Topics covered
- Workflow orchestration and state management
- Graph-based workflows and conditional routing
- Connecting RAG pipelines with AI agents
- Tool selection and task execution
- Introduction to LangGraph and multi-agent collaboration
- Execution tracing, monitoring, and agent evaluation
Tools & tech
PythonLangChain
LangGraphVector DatabasesLLM APIs
RAG FrameworksWhat you'll build
Intelligent Document Research Agent with Evidence-Based Reports
Prepare AI applications for practical use through testing, deployment, security, and responsible AI practices.
Topics covered
- Deploying AI applications using interactive web interfaces
- Introduction to FastAPI and Docker
- Functional testing and regression testing
- Managing API keys, credentials, and application configuration
- Monitoring latency, reliability, and usage costs
- Data privacy, access control, prompt injection, and AI safety
Tools & tech
PythonStreamlit
FastAPI
DockerGit
Testing FrameworksWhat you'll build
Deployed AI Application with Testing and Basic Security Controls
Apply the complete learning experience to design, develop, test, and demonstrate an AI-powered solution for a real-world problem.
Topics covered
- Problem identification and defining user requirements
- Application architecture and technology selection
- Integrating Generative AI, RAG, and AI agents
- Developing and testing an end-to-end application
- Evaluating outputs, documenting limitations, and improving performance
- Preparing project documentation and presenting technical decisions
Tools & tech
PythonLLM APIsRAG Frameworks
AI Agent FrameworksStreamlitGitWhat you'll build
End-to-End AI Application Using Generative AI, RAG & AI Agents
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