Program Highlights
Top-Notch Faculty
Assignments and Case Studies
Real-life Capstone Projects
Placement Assistance
Skills Covered
Learn next-generation AI skills in our Gen AI and Agentic AI training, from crafting powerful prompts to building autonomous agents that can reason, retrieve knowledge, and execute complex tasks.
Generative AI
Generative AI
Prompt Engineering
ChatGPT & LLMs
RAG Pipeline Development
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Agentic AI
AI Agents & Agentic AI
Multi-Agent Systems
Vector Databases
API Development & Deployment
Model Serving & Observability
Tools Covered
Master an industry-ready stack of tools and earn our Gen AI and Agentic AI certification to build, deploy, and scale intelligent solutions.
ChatGPT
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GPT (GPT-3, GPT-4, GPT-5)
DALL.E 3
Stable Diffusion
Mistral
Llama
LangChain
LlamaIndex
HuggingFace
Python
Streamlit
Gradio
Pinecone
Milvus
FAISS
Agentic AI
LangGraph
AutoGen
CrewAI
FastAPI
Docker
MLFlow
LangSmith
Pydantic
Projects
- Domain: Banking & Financial Services
- Banks and financial institutions face high volumes of customer queries ranging from account inquiries to loan eligibility questions. Traditional call centres are costly and inefficient. This project involves developing an AI-powered chatbot using LLMs and task-specific agents to automatically resolve customer queries, provide personalised financial advice, and escalate complex issues to human agents seamlessly.
- Domain: Retail & E-commerce
- Retailers struggle to dynamically price products in a competitive market while balancing profit and market share. This project, offered in our agentic AI and gen AI course, leverages AI models and multi-agent systems to predict optimal product prices based on historical sales, market trends, and competitor pricing. Agents simulate market scenarios to recommend pricing strategies for maximum revenue and sales conversion.
- Domain: Manufacturing & Industrial IoT
- Factories face inefficiencies due to unoptimised workflows, machine downtime, and quality control issues. This project implements AI-driven predictive maintenance and workflow optimisation using LLM-based decision agents. The system analyses sensor data, identifies potential failures, and recommends process adjustments to improve production efficiency and reduce waste.
- Domain: Education Technology
- Students have diverse learning needs, and one-size-fits-all education platforms often fail to keep learners engaged. This project, as part of generative AI and agentic AI training, develops an AI-powered learning assistant using LLMs that creates personalised learning paths, answers student queries, and recommends exercises. Agents continuously adapt content based on student performance and learning pace.
- Domain: Healthcare & Life Sciences
- Medical professionals often need to analyse large volumes of patient data to arrive at accurate diagnoses, which is time-consuming and prone to error. This project uses LLMs and specialised diagnostic agents to assist doctors in interpreting patient records, identifying patterns in medical data, and suggesting probable diagnoses, thus improving accuracy and reducing diagnostic time.
- Domain: Legal Tech
- Law firms and corporations spend significant time reviewing contracts and legal documents for compliance and risk management. This project, aligned with our generative AI and agentic AI certification, employs LLMs and document-understanding agents to automatically analyse contracts, identify critical clauses, flag potential risks, and summarise key information. This reduces manual effort and accelerates legal workflows.
Roles and Salary Trends
Learning Path
Why ExcelR
Industry-Based Course Curriculum
Work Hands-on with 25+ Assignments & 5+ Capstone Projects
Placement Assistance with our 5000+ partner companies
Support through WhatsApp, Calls, & Emails
eLearning Access
Course Curriculum
Generative AI & Agentic AI
- Foundations of Generative AI
- History of Chat GPT models
- Frontier models from Google, Anthropic etc
- Foundations of AI & NLP
- Example applications using sequence-to-sequence models
- Introduction to Generative AI
- variables
- loops
- functions
- modules
- libraries
- Machine Learning and Deep Learning
- NLP basics: tokenization
- embeddings
- TF-IDF
- Word2Vec
- GloVe
- Advanced NLP
- Transformers
- Attention mechanism
- Encoder-decoder architectures
- Advanced Applications of Generative AI
- Variational Autoencoders, Generative Adversarial Networks
- Generative Pretrained Transformer models, Large Language Models
- Intro to prompt engineering
- Different Types of Prompting
- Zero Shot, Single Shot, Multi Shot, Chain of Thought
- Use cases in ChatGPT
- Practical coding tasks and automating ML project workflows
- Chatbot Application for customer service
- Experimenting with different GPT architectures (GPT-3 to GPT-5)
- Responsible and Ethical AI fundamentals
- What are Guardrails, Different types of Guardrails
- Best Industry Practices
- What is RAG
- Terminology in RAG
- RAG core concepts
- Types of RAG, Applications of RAG
- AI hallucinations
- Semantic Search
- Challenges in RAG
- RAG vs Fine-Tuning
- RAG technique evaluation methods
- LLM as judge
- Ground truth evaluation
- RAGAS framework and use cases
- Intro to Vector Databases
- Pinecone,
- Milvus,
- FAISS,
- Comparative Analysis between vector databases
- LangChain ecosystem
- Installation & Setup, Core LangChain modules
- LangChain agents
- LangChain memory
- LangChain embeddings
- Data ingestion
- Intro to LangGraph
- LangGraph ecosystem
- LangGraph Ecosystem-Workflows, nodes, edges, state, execution
- Intro to LangSmith
- LangSmith ecosystem
- LangSmith concepts- tracking, evaluation, debugging, integration
- Mini project (multi-agent workflow)
- Intro to Small Language Models
- Advantages of Small Language Models
- Popular models
- Key features
- Performance, architectures, applications
- Intro to Llama Index concepts
- Llama Index ecosystem- indexing, retrieval, memory
- LLama Index Implementation, tools, use cases with small language models
- Open-Source vs Closed Source
- Benefits of Open Source
- Examples like Mistral and Llama
- Beyond the black box, Constraints and Challenges
- More open-source models - Qwen, Hugging Face
- Applications, Costing, Customization
- Security and privacy features
- Choosing LLMs
- Hugging Face leaderboards and benchmarks
- Different categories - Reasoning, Math, Coding etc
- More Hugging Face benchmarks for AI agents
- Tool use, Domain Specific - Legal, Finance, Medical etc
- Evaluation Metrics
- Perplexity, Rouge
- BLEU, Accuracy
- F1, efficiency metrics and use cases
- Latest fine-tuning developments
- OSS vs closed models
- Unsupervised Fine-tuning, Supervised Fine-tuning
- Instruction Fine-tuning methods
- Intro to Popular Fine-tuning techniques
- PEFT, LoRA, QLoRA, SFT
- GRPO applications and use cases
- What is Quantization
- Terminology in Quantization
- Popular techniques such as AWQ, GGUF, GPTQ, EXL2
- AI Agents vs Agentic AI
- Historical Foundations of Agents
- Timeline of Agents
- Formal Taxonomy Framework
- Application of Agents
- Why Pydantic
- Coding for Agentic AI
- Pydantic basics & building robust applications
- Multi step Agentic workflows
- Hands-on: Installation and Setup
- Agent Tools, Prebuilt Agent
- Working with Custom Agent and LangGraph
- Recent Developments in RAG
- Agentic RAG, Agentic AI Architecture
- Agentic AI knowledge
- Key features - Orchestration & Intelligence
- Benefits of Agentic RAG
- Prompt chaining, deep dive into LLM Agents
- Understanding LLM drift Prompt Drift, Cascading
- Unifying Large Language Models
- Knowledge Graph and Graph RAG concepts
- Applications using Knowledge Graph and Graph RAG
- Advantages and Disadvantages of Knowledge RAG techniques
- Unimodal to Multimodal
- Leading MLLMs, Architectures and Data Fusion
- Building Applications
- Core challenges and Limitations
- Risks and Future of MLLMs
- New Generation Applications in Audio and Video
- Different MLLM Architectures
- Comparison between frontier MLLM models
- Ethics of MLLM models
- Anatomy of Diffusion models
- Forward/reverse process
- Backbone architecture of Diffusion models
- Challenges and Limitations of Diffusion models
- Hands-on - Hugging Face/diffusers models
- Stability AI APIs, Text to Image applications
- Image to Image examples
- Cost vs Quality Trade-off
- Future of Diffusion models
- Elements of Reinforcement Learning
- Different RL algorithms
- Q-learning, Exploration & exploitation
- Problem Solving methods in RL
- Advanced RL models
- Policy gradient, Actor-critic
- Proximal Policy Optimization
- Discussion on Trending RL models
- Working with Deep RL libraries
- OpenAI Gym, TensorFlow Agents etc
- LLM operations, Deployment strategies
- Hardware requirements, APIs
- Docker containerization
- Testing and Managing Deployment
- LLM operations and Deployment Concepts
- Applications using frameworks such as Fast API, Streamlit, etc.
- Working with different Model Serving Frameworks
- Popular Frameworks like MLFlow, Gradio, Chainlit;
- Cost and latency strategies in frameworks
- LLM Monitoring - Average API costs, Token costs, Dashboard metrics
- Advanced tracing of LLM models using LangSmith and LangFuse
- Discussion on Open-source observability engineering platforms
- What is MCP
- Model Context Protocol ecosystem
- Tool integration, MCP repository
- MCP server in Claude
- MCP architecture
- End to End Project Deployment with industry leading tools
- Deployment & Capstone Project- LangGraph +LangSmith Frameworks
- Creative and Final Project on Image & Video creation
- Working with tools such as GPT-5, ImageGen 4 etc.
- Capstone Presentation
- Academic Use Case
- End to End LLM Project Deployment (LLMs) with LangGraph Framework and its ecosystem
- Academic Use Case
- End to End Multimodal Project Deployment (MLLMs) as LangGraph and its ecosystem
- End to End Development of POC
- Build a RAG-enabled assistant using LangGraph ecosystem and tools
- Industry Use Case
- End to End Multimodal Project Deployment (MLLMs) with RAG and industry leading tools
- Intro to AutoGen Framework
- Intro to Foundation Layer, Agents, Runtime concepts
- Understanding Communication Patterns, Tools, Model Clients, Memory etc.
- End to End Use Case
- Building agents with AutoGen Ecosystem and its Environment,
- Important Design Principles
- Intro to Crew AI Framework
- Architecture, Roles, Autonomy and Focus
- Safety Guardrails
- Use case of Agents with Crew AI
- Understanding Model-Content Prompts (MCP)
- Prompt Templates, Patterns for Multi-Role Agents
- Agentic AI Use case with MCP server Architecture
- Deployment Architecture patterns
- Deployment environments on Cloud and on premises
- Containerization and Orchestration, Scaling Strategies
- Monitoring, Memory and Storage Requirements
- What is Model Serving
- Model Serving Architecture patterns
- Model Serving frameworks and platforms
- Serving optimization techniques for Agentic LLM workflows
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