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AI transforming every industry



Growing demand for AI-skills



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Opportunities for freshers & professionals



Future-focused career paths

WEEK 1 — AI & LLM FUNDAMENTALS


AI & Generative AI

  • AI vs ML vs Deep Learning vs Generative AI
  • Generative AI applications
  • LLMs & Foundation Models
  • How LLMs work
  • Tokens & Tokenization
  • Context Window
  • Parameters
  • Training vs Inference
  • Pre-training & Fine-tuning
  • Transformer Architecture — fundamentals
LLM Concepts
  • Open-source vs closed-source LLMs
  • LLM APIs
  • Model selection
  • Temperature
  • Max Tokens
  • System & User Prompts
  • Structured Outputs
Practical

Build a basic AI chatbot

Connect to an LLM API

Generate text

WEEK 2 — PROMPT ENGINEERING + LLM APPLICATIONS


Prompt Engineering:

  • Prompt structure
  • Zero-shot prompting
  • Few-shot prompting
  • Role prompting
  • Instruction prompting
  • Context injection
  • Output formatting
  • Structured JSON output
  • Prompt templates
  • Prompt chaining
Advanced Prompting:
  • ReAct concepts
  • Self-reflection
  • Context management
  • Hallucination reduction
LLM Applications
  • Text generation
  • Summarization
  • Classification
  • Information extraction
  • Question answering
🎯 PROJECT 1

AI Content & Document Assistant

WEEK 3 — EMBEDDINGS + VECTOR DATABASES

Embeddings

  • What are embeddings?
  • Text embeddings
  • Vector representations
  • Semantic similarity
  • Similarity search
Vector Databases
  • Why Vector Databases?
  • Chroma
  • FAISS
  • Pinecone concepts
  • Collections
  • Metadata
  • Vector Search
  • Top-K retrieval
🎯 PROJECT 2

Semantic Search Engine

Documents

    ↓

Embeddings

    ↓

Vector Database

    ↓

Similarity Search

    ↓

Relevant Results

WEEK 4 — RAG


Retrieval-Augmented Generation

RAG Fundamentals
  • What is RAG?
  • Why RAG?
  • RAG architecture
  • Document loaders
  • Text splitting
  • Chunking strategies
  • Embeddings
  • Vector databases
  • Retrieval
  • Context injection
  • LLM generation
Advanced RAG
  • Similarity search
  • Metadata filtering
  • Top-K retrieval
  • Hybrid search
  • Query transformation
  • Reranking
  • RAG hallucinations
🎯 PROJECT 3

AI PDF / Document Q&A System

WEEK 5 — LANGCHAIN

LangChain Fundamentals
  • LangChain architecture
  • Models
  • Prompts
  • Output parsers
  • Runnables
  • Chains
  • Document loaders
  • Text splitters
  • Retrievers
  • Vector stores
LangChain Applications
  • Prompt chains
  • Sequential workflows
  • Retrieval chains
  • RAG applications
  • Structured outputs
Memory
  • Conversation history
  • Context management
  • Chat history
  • Persistent memory concepts
🎯 PROJECT 4

AI Knowledge Assistant using LangChain + RAG

WEEK 6 — LANGSMITH + LLM OBSERVABILITY


LangSmith

Fundamentals
  • What is LangSmith?
  • LangSmith architecture
  • Connecting LangChain applications
  • Tracing
  • Runs
  • Projects
  • Monitoring LLM applications
Debugging
  • Trace LLM calls
  • Trace chains
  • Trace tools
  • Trace retrieval
  • Identify failures
  • Debug prompts
  • Debug RAG pipelines
  • Analyze latency
  • Monitor token usage
Evaluation
  • Dataset creation
  • Test cases
  • LLM evaluation
  • Rule-based evaluation
  • Evaluators
  • Response quality
  • Retrieval quality
  • Hallucination evaluation
Production Monitoring
  • Monitoring applications
  • Feedback
  • Error analysis
  • Performance tracking
  • Prompt/version management
🎯 PROJECT 5

Production-Ready RAG Application with LangSmith Monitoring & Evaluation•Social Media Strategy (Organic + Paid)

WEEK 7 — AI AGENTS + TOOL CALLING


Agentic AI Fundamentals

  • LLM vs AI Application vs AI Agent
  • What is Agentic AI?
  • Agent architecture
  • Goals
  • Planning
  • Reasoning
  • Tools
  • Memory
  • Actions
  • Observation
  • Feedback loops
Tool Calling
  • Function calling
  • Tool definitions
  • Tool execution
  • API tools
  • Database tools
  • Search tools
  • Calculator tools
  • Custom tools
Agent Patterns
  • ReAct
  • Tool-using agents
  • Planning agents
  • Reflection
  • Multi-step tasks
  • Human-in-the-loop
🎯 PROJECT 6

AI Research Agent

User Question > Agent > Planning > Tool Selection > Search / APIs / Database > Analysis > Final Answer

WEEK 8 — LANGGRAPH + MULTI-AGENT SYSTEMS


LangGraph

  • LangGraph fundamentals
  • Graph-based workflows
  • Nodes
  • Edges
  • State
  • State management
  • Conditional routing
  • Loops
  • Checkpoints
Agent Workflows
  • Sequential agents
  • Conditional agents
  • Planning agents
  • Reflection agents
  • Human approval
  • Agent memory
Multi-Agent Systems
  • Supervisor agent
  • Worker agents
  • Specialized agents
  • Agent communication
  • Task delegation
🎯 PROJECT 7

Multi-Agent Research & Report Generation System

             Supervisor Agent

                     ↓

       ┌─────────────┼─────────────┐

       ↓             ↓             ↓

 Research Agent   Data Agent   Writer Agent

       ↓             ↓             ↓

       └─────────────┼─────────────┘

                     ↓

                Final Report

WEEK 9 — MCP + A2A + PRODUCTION AI


MCP — Model Context Protocol

  • What is MCP?
  • MCP architecture
  • MCP Client
  • MCP Server
  • Resources
  • Tools
  • Prompts
  • Connecting LLMs to external systems
  • Building a basic MCP server
A2A — Agent-to-Agent
  • A2A concepts
  • Agent discovery
  • Agent communication
  • Task delegation
  • Multi-agent workflows
Production AI
  • FastAPI
  • REST APIs
  • Streaming
  • Authentication
  • Async operations
  • Environment variables
  • Docker basics
  • AI application architecture
  • Deployment basics
Observability

LangSmith

  • FastAPI
  • Production feedback
  • Tracing
  • Evaluation
  • Monitoring
  • Debugging
WEEK 10 — END-TO-END AGENTIC AI CAPSTONE


Capstone: AI Business Automation Platform

Build a complete production-style Agentic AI system.

                        USER

                           ↓

                    AI ASSISTANT

                           ↓

                    SUPERVISOR AGENT

                           ↓

          ┌────────────────┼────────────────┐

          ↓                ↓                ↓

    Research Agent     Data Agent       Action Agent

          ↓                ↓                ↓

          └────────────────┼────────────────┘

                           ↓

                         RAG

                           ↓

                    VECTOR DATABASE

                           ↓

                    EXTERNAL TOOLS

                           ↓

                    FINAL RESPONSE

                           ↓

                     LANGSMITH

                 Trace + Evaluate

Capstone Technologies
  • Deployment
  • Python
  • LLM API
  • Prompt Engineering
  • Embeddings
  • Vector Database
  • RAG
  • LangChain
  • LangSmith
  • AI Agents
  • LangGraph
  • Multi-Agent Systems
  • MCP
  • A2A
  • Memory
  • Tool Calling
  • FastAPI
  • Database
  • Evaluation
  • Monitoring
  • Docker

Who Can Join?

What You’ll Learn?

AI Fundamentals

AI Tools

Practical Skills

Automation

Projects & Portfolio

Career Preparation

Hands-On Learning

Exposure to Live Projects
Real-World Assignments
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Automation Workflows
Solving Case Studies
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