Build a strong mental model of AI and the modern LLM revolution — what changed, why it matters, and how it’s reshaping every industry.

• Introduction to AI, its evolution, and the modern AI revolution
• Types of AI: Narrow, General, and Generative
• How LLMs work: tokens, context windows, and inference
• Training vs Inference (conceptual, no math)
• Industry use cases across Healthcare, BFSI, Retail, EdTech, and more
• Overview of GPT, Gemini, and Claude — strengths and ideal use cases

Hands-on: Explore ChatGPT-like tools and walk through real-world AI applications

Move from casual prompt user to confident prompt designer — extract maximum value, accuracy, and reliability from any LLM.

• Fundamentals of prompts and how LLMs interpret them
• Prompt techniques: Role-based, Few-shot, Chain-of-thought, Instruction-based
• Prompt optimization: tone, formatting, reducing hallucinations, structured outputs

Hands-on: Prompt design exercises and real business use-case prompts.

A focused, beginner-friendly introduction to Python — the foundation of every AI workflow.

• Variables, data types, lists, dictionaries, sets
• Conditional statements, loops, and functions
• String manipulation for prompts and outputs
• File handling, error handling, and debugging

Hands-on: Build Python mini-projects and clean text data for AI pipelines.

Step into programmatic AI — the foundation of every modern AI product.

• API fundamentals and authentication
• Accessing OpenAI, Gemini, and Claude programmatically
• Sending prompts, handling responses, and managing parameters
• Best practices: rate limits, retries, error handling

Hands-on: Build your first AI-powered Python script using a real LLM API.

Master one of the most widely adopted frameworks for building production-grade LLM applications.

• Introduction to LangChain — architecture and ecosystem
• Prompt templates, chains, and memory
• Building conversational AI systems with state and continuity

Hands-on: Build a fully functional chatbot using LangChain.

The cornerstone technique behind every modern enterprise AI assistant.

• RAG architecture and why it matters
• Embeddings and semantic vector search
• Vector databases — focused study on FAISS

Hands-on: Store and retrieve knowledge using vector embeddings.

Build complete document-aware AI applications with one of the most powerful RAG toolkits.

• LlamaIndex fundamentals and architecture
• Document ingestion and chunking strategies
• Indexing strategies and retrieval optimization

Hands-on: Build a fully functional PDF-based AI chatbot.

Understand the next evolution of GenAI — autonomous agents that reason, plan, and act.

• • What AI agents are and how they’re built
  • Agent vs chatbot — a clear comparison
  • Automation and decision-making use cases across industries

• Control vs autonomy
• Single task vs multi-step goal completion

Where multiple specialized agents collaborate to solve problems no single model can.

• CrewAI and AutoGen frameworks
• Designing agent roles and collaboration patterns
• Task delegation and coordination

Hands-on: Build a multi-agent workflow that simulates a real business team.

Production-grade agent design with stateful workflows and multi-step reasoning.

• LangGraph fundamentals — graphs, nodes, edges, state
• Designing stateful workflows for complex agents
• Multi-step reasoning and human-in-the-loop patterns

Hands-on: Build an intelligent agent pipeline using LangGraph.

Build full AI workflows visually — perfect for rapid prototyping and business users.

• Langflow interface and component library
• Drag-and-drop AI workflow design

Hands-on: Build a fully functional AI chatbot without coding.

Combine AI with workflow orchestration to automate real business operations.

• n8n workflow fundamentals
• Triggers, actions, and integrations across third-party tools

Hands-on: Automate real business processes using AI-powered workflows

Turn AI prototypes into deployable products with a real backend and UI.

• FastAPI backend basics
• Streamlit frontend UI
• API + UI integration

Hands-on: Build a complete full-stack AI application end-to-end.

Use the new generation of AI coding tools as a productivity multiplier.

• Claude Code and Cursor — capabilities and best practices
• AI-powered code generation, refactoring, and debugging

Use the new generation of AI coding tools as a productivity multiplier.

• • Claude Code and Cursor — capabilities and best practices
  • AI-powered code generation, refactoring, and debugging

A panoramic view of the broader AI ecosystem every practitioner should know.

• Perplexity AI — AI-powered research
• Hugging Face — models, datasets, and the open-source ecosystem

Production AI demands observability — monitor, trace, and continuously improve.

• Langfuse and LangSmith for tracing and evaluation
• Debugging AI workflows and performance monitoring

Architectural concepts essential for taking AI from prototype to production scale.

• MCP (Model Context Protocol) — the emerging standard for AI tool integration
• Scaling AI systems and production-level considerations

Industry-ready projects that consolidate the full curriculum into deployable, portfolio-worthy builds.

• Enterprise RAG Chatbot
• Multi-Agent Automation System
• AI Data Assistant
• AI SaaS Web Application
• AI Workflow Automation
• Agentic RAG System
• AI Coding Assistant