Course Duration
01
2 Months
Structured learning path
Generative AI + Agentic AI
LLMs • RAG • LangChain • LangGraph • Multi-Agent Systems • Automation
LLM
RAG
Agent
Tools
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Curriculum
02
18 Modules
Step-by-step learning
Hands-On
03
Real-Time Projects
Build practical applications
Career Support
04
Interview Prep
Placement-focused guidance
Complete Curriculum
Generative AI & Agentic AI Learning Roadmap
18 practical modules planned across a 16-week learning path. Click any module to view the topics covered.
Modules
Month 1
Month 2
Month 3
Month 4
Learning Plan
W1
W2
W3
W4
W5
W6
W7
W8
W9
W10
W11
W12
W13
W14
W15
W16
AI, Generative AI & Agentic AI Foundations
- AI, Machine Learning, Deep Learning and Generative AI overview
- Foundation models, LLMs and multimodal AI
- What makes an AI system agentic
- Generative AI vs AI agents vs workflow automation
- Real-world use cases across software, CRM, analytics and operations
Python for Generative & Agentic AI
- Python syntax, functions, classes and modules
- Working with APIs, JSON and environment variables
- Async programming fundamentals
- Virtual environments and package management
- Reusable utility layers for AI applications
LLM Fundamentals
- Tokens, context windows and embeddings
- Transformer architecture concepts
- Pretraining, fine-tuning and inference
- Temperature, top-p and structured generation
- Comparing OpenAI, Claude, Gemini and open-source models
Prompt Engineering & Reasoning Patterns
- Zero-shot, one-shot and few-shot prompting
- Role prompting and structured prompt design
- Task decomposition and reasoning workflows
- Reusable prompt templates
- Prompt testing, evaluation and improvement
OpenAI, Claude & Gemini APIs
- API authentication and model invocation
- Text generation and structured outputs
- Streaming responses
- Tool / function calling fundamentals
- Building provider-agnostic AI integrations
Embeddings, Vector Databases & Semantic Search
- Embedding concepts and similarity search
- Document chunking and indexing strategies
- FAISS, Chroma and Pinecone concepts
- Metadata filtering
- Building semantic search pipelines
RAG Applications
- Retrieval-Augmented Generation architecture
- Document ingestion pipelines
- Retriever and context construction
- Grounded answers and citation-ready responses
- Building enterprise knowledge assistants
LangChain & LlamaIndex
- Prompt templates and chains
- Retrievers, loaders and text splitters
- Memory and tool integration
- LlamaIndex query engines and indexes
- Building modular enterprise GenAI workflows
AI Agent Fundamentals
- Agents, goals, actions and environments
- Agent loops and execution cycles
- Single-agent architecture
- Planning, reflection and self-correction
- Agent reliability and failure handling
Tools, Function Calling & Agent Memory
- Designing safe agent tools
- Function calling and structured tool inputs
- Short-term and long-term memory
- Vector memory and semantic recall
- Connecting agents to APIs, files and databases
LangGraph & Stateful Agent Workflows
- Graph-based agent architecture
- Nodes, edges and shared state
- Conditional routing
- Human-in-the-loop workflows
- Building robust multi-step agent systems
Multi-Agent Systems
- Role-based agent design
- Coordinator, supervisor and worker patterns
- Agent-to-agent communication
- Task delegation and collaboration
- Designing reliable multi-agent workflows
CrewAI, AutoGen & Team-Based Agents
- CrewAI agents, crews and tasks
- Hierarchical and sequential processes
- AutoGen conversational agent concepts
- Group chat and collaborative workflows
- Use cases for coding, research and operations
Agentic RAG & Enterprise Knowledge Agents
- Dynamic retrieval inside agent workflows
- Agent-generated search queries
- Multi-step knowledge reasoning
- Tool-based retrieval and verification
- Building research and internal knowledge agents
Browser, CRM & Workflow Automation Agents
- Browser automation concepts
- API orchestration
- CRM lead and follow-up automation
- Document and file-processing agents
- Safe execution boundaries and permissions
Security, Guardrails & Observability
- Prompt injection and tool-abuse risks
- Input/output validation
- Permissions and least-privilege tool access
- Logging, tracing and monitoring
- Evaluating agent decisions and production safety
Production Deployment & Real-Time Projects
- FastAPI backend for GenAI and agent systems
- Redis/background jobs concepts
- Docker and cloud deployment
- AWS production architecture fundamentals
- Real-time projects: support agent, CRM agent, research agent and RAG assistant
Capstone Project & Placement Preparation
- End-to-end Generative + Agentic AI capstone
- Multi-agent architecture and deployment
- GitHub portfolio and technical documentation
- Resume, LinkedIn and project presentation
- Mock interviews and placement preparation
Select a module to view the detailed topics.
Hands-on Learning
Capstone Projects
Practice real application flows while building projects you can discuss during interviews.
We Learn – Groceries Application
We Learn – Food Application
We Learn – Banking Application
We Learn – Delivery Application
We Learn – Airlines Application
We Learn – LMS Application
Career Preparation
Build Job-Ready Profiles
Present your skills professionally across the platforms recruiters commonly review.
GitHub
Showcase code, projects, repositories and consistent development work.
Build a stronger professional identity and improve recruiter visibility.
Portfolio
Present your projects, technical skills and practical experience clearly.
AI Skills
Learn the AI Tools Used by Modern Developers
Use AI to code faster, solve problems, build projects, prepare for interviews and stay ready for modern software jobs.
✦ChatGPT
AIClaude
GeminiGitHub Copilot
Cursor AI
PerplexityAIOpenAI API
LangChain
🤗Hugging Face
Replit AIWindsurf AI
AI Agents
AI for Coding
Write, debug and understand code faster with modern AI coding assistants.
AI for IT Jobs
Prepare resumes, interviews, projects and workplace tasks using AI.
AI for Career Growth
Stay updated with tools used by developers, startups and product teams.
Student Outcomes
Our Alumni
Surya
Infosys
Farhath
L&T
Manasa
Capgemini
Sana Lekana
Tech Mahindra
Ganesh
Genpact
Gnaneshwar
Amazon
Ravi
Hexaware
Harinath
AdobeSurya
InfosysFarhath
L&TManasa
CapgeminiSana Lekana
Tech MahindraGanesh
GenpactGnaneshwar
AmazonRavi
HexawareHarinath
AdobeRavi
HexawareHarinath
AdobeSurya
InfosysFarhath
L&TManasa
CapgeminiSana Lekana
Tech MahindraGanesh
GenpactGnaneshwar
AmazonRavi
HexawareHarinath
AdobeSurya
InfosysFarhath
L&TManasa
CapgeminiSana Lekana
Tech MahindraGanesh
GenpactGnaneshwar
Amazon
Common Questions
Frequently Asked Questions
Everything you need to know about our Generative AI & Agentic AI Training Program.
You will learn LLM fundamentals, prompt engineering, OpenAI/Claude/Gemini APIs, embeddings, vector databases, RAG, LangChain, LlamaIndex, AI agents, LangGraph, multi-agent systems, CrewAI, AutoGen, security, automation and production deployment.
No. The curriculum starts with AI, Generative AI and Agentic AI foundations, then introduces Python and LLM concepts before moving into advanced agent workflows.
Yes. The course includes embeddings, similarity search, FAISS, Chroma and Pinecone concepts, document chunking, indexing, semantic search and complete Retrieval-Augmented Generation pipelines.
Yes. You will learn LangChain and LlamaIndex for modular GenAI workflows, plus LangGraph for stateful, multi-step and human-in-the-loop agent systems.
Yes. The curriculum covers agent loops, goals, tools, planning, memory, reflection, supervisors, workers, task delegation, agent collaboration and multi-agent architecture.
Yes. One module covers CrewAI agents, crews and tasks, plus AutoGen conversational-agent concepts, group chat and collaborative workflows.
Yes. The course includes prompt-injection risks, tool permissions, input/output validation, logging, tracing, FastAPI, Redis/background jobs, Docker, cloud deployment and AWS architecture fundamentals.
The final module includes an end-to-end Generative AI and Agentic AI capstone with multi-agent architecture, deployment, GitHub documentation, resume and LinkedIn presentation, mock interviews and placement preparation.
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