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Generative AI & Agentic AI|Telugu

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Master Generative AI, Agentic AI, AI Agents, RAG and MCP from basic to advanced level. Build real-world AI projects and become job-ready as an AI Engineer, Generative AI Engineer, AI Agent Developer, RAG Engineer or LLM Engineer.

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Course syllabus

01Python Introduction
  • What is Programming?
  • What is Coding?
  • What is a Programming Language?
  • Introduction to Python
  • History of Python
  • Features of Python
  • Advantages of Python
  • Python versions
  • Python 2 vs Python 3
  • Python Interpreter
  • Python Library
  • Python Modules
  • Python Packages
  • Python Frameworks
  • Python Web Frameworks
  • Python Flavours
  • What can Python do?
  • Why Python?
  • Python syntax
  • Python syntax compared with other programming languages
  • Python use cases
  • Python for Web Development
  • Python for Automation
  • Python for Data Science
  • Python for Artificial Intelligence
  • Python for Machine Learning
  • Python Installation
  • Checking Python installation
  • Running the first Python program
02Integrated Development Environment Installation
  • What is an IDE?
  • Why IDEs are used
  • Introduction to Visual Studio Code
  • Installing Visual Studio Code
  • Installing Python extension
  • Creating a Python project
  • Creating Python files
  • Running Python programs
  • Using the integrated terminal
  • Python interpreter configuration
  • Installing Python packages
  • Debugging Python applications
  • Breakpoints
  • Code formatting
  • Code navigation
  • Introduction to PyCharm
  • Basic IDE shortcuts
03Fundamentals of Python
  • Python syntax
  • Python indentation
  • print() function
  • Comments
  • Single-line comments
  • Multi-line comments
  • Python variables
  • Variable naming rules
  • Python keywords
  • Constants
  • Python data types
  • Numbers
  • Integers
  • Floating-point numbers
  • Complex numbers
  • Strings
  • Lists
  • Tuples
  • Sets
  • Dictionaries
  • Boolean data type
  • None type
  • type() function
  • Type conversion
  • Implicit type conversion
  • Explicit type conversion
  • int()
  • float()
  • str()
  • bool()
  • input() function
  • Basic input and output
  • Output formatting
04Python Operators
  • What are operators?
  • Arithmetic operators
  • Addition
  • Subtraction
  • Multiplication
  • Division
  • Floor division
  • Modulus
  • Exponentiation
  • Assignment operators
  • Comparison operators
  • Equal to
  • Not equal to
  • Greater than
  • Less than
  • Greater than or equal to
  • Less than or equal to
  • Logical operators
  • and
  • or
  • not
  • Identity operators
  • is
  • is not
  • Membership operators
  • in
  • not in
  • Operator precedence
  • Operator associativity
  • Practical operator examples
05Python Conditional Statements
  • Introduction to decision making
  • Indentation
  • if statement
  • if condition with examples
  • if-else statement
  • if-elif-else statement
  • Multiple conditions
  • Nested if statements
  • Short-hand if
  • Short-hand if-else
  • Conditional expressions
  • Combining conditions
  • Logical conditions
  • Comparison operators with conditions
  • Membership operators with conditions
  • Student grading example
  • Age verification example
  • Login validation example
  • Even and odd number example
  • Real-time conditional programming exercises
06Python Iterative Statements
  • Introduction to loops
  • Why loops are required
  • for loop
  • while loop
  • range() function
  • Looping through strings
  • Looping through lists
  • Looping through tuples
  • Looping through dictionaries
  • Nested for loops
  • Nested while loops
  • Combining loops with conditions
  • Infinite loops
  • Loop control
  • Pattern programs
  • Number series programs
  • Table generation
  • Factorial using loops
  • Real-time looping examples
07Python Jumping Statements
  • Introduction to jumping statements
  • break statement
  • break with for loop
  • break with while loop
  • continue statement
  • continue with loops
  • pass statement
  • Difference between break and continue
  • Difference between continue and pass
  • Nested loop control
  • Real-time examples
  • Practical programming exercises
08Python Strings
  • Introduction to strings
  • Creating strings
  • Single quotes and double quotes
  • Multi-line strings
  • String indexing
  • Positive indexing
  • Negative indexing
  • String slicing
  • String concatenation
  • String repetition
  • String immutability
  • String methods
  • upper()
  • lower()
  • capitalize()
  • title()
  • strip()
  • replace()
  • split()
  • join()
  • find()
  • count()
  • startswith()
  • endswith()
  • String formatting
  • format() method
  • f-strings
  • Escape characters
  • Real-time string examples
09Python Lists
  • Introduction to Lists
  • Creating Lists
  • List indexing
  • List slicing
  • Updating list elements
  • Adding elements
  • Removing elements
  • append()
  • insert()
  • extend()
  • remove()
  • pop()
  • clear()
  • sort()
  • reverse()
  • copy()
  • Nested lists
  • Looping through lists
  • List comprehensions
  • Conditional list comprehensions
  • Lists as stacks
  • Lists as queues
  • Real-time list examples
10Python Tuples
  • Introduction to Tuples
  • Creating Tuples
  • Tuple indexing
  • Tuple slicing
  • Tuple immutability
  • Tuple packing
  • Tuple unpacking
  • Nested tuples
  • Tuple operations
  • Tuple built-in functions
  • count()
  • index()
  • Using tuples in real-time applications
  • Difference between List and Tuple
11Python Sets
  • Introduction to Sets
  • Creating Sets
  • Set properties
  • Adding elements
  • Removing elements
  • add()
  • remove()
  • discard()
  • pop()
  • clear()
  • Set union
  • Set intersection
  • Set difference
  • Symmetric difference
  • Subset
  • Superset
  • Set membership
  • Frozen sets
  • Real-time set examples
12Python Dictionary
  • Introduction to Dictionaries
  • Creating dictionaries
  • Dictionary keys and values
  • Accessing dictionary values
  • Adding dictionary items
  • Updating dictionary items
  • Removing dictionary items
  • keys()
  • values()
  • items()
  • get()
  • update()
  • pop()
  • popitem()
  • clear()
  • Nested dictionaries
  • Looping through dictionaries
  • Dictionary comprehensions
  • Real-time dictionary examples
13Python Functions
  • Introduction to Functions
  • Why functions are required
  • Defining a function
  • Calling a function
  • return statement
  • Difference between return and print
  • Arguments
  • Parameters
  • Positional arguments
  • Keyword arguments
  • Default arguments
  • Arbitrary arguments
  • *args
  • **kwargs
  • User-defined functions
  • Built-in functions
  • Nested functions
  • Recursive functions
  • Lambda functions
  • Variable scope
  • Local variables
  • Global variables
  • global keyword
  • Function documentation
  • Real-time function examples
14Python Modules & Packages
  • What is a module?
  • Creating custom modules
  • Importing modules
  • import statement
  • from import statement
  • Import aliases
  • Built-in modules
  • math module
  • random module
  • datetime module
  • os module
  • sys module
  • What is a package?
  • Creating packages
  • Package structure
  • __init__.py
  • Installing external packages
  • pip
  • requirements.txt
15Object-oriented Programming with Python
  • Introduction to Object-Oriented Programming
  • Advantages of OOP
  • Classes
    • What is a class?
    • Creating classes
    • Class attributes
    • Class methods
    • Real-time class examples
  • Objects
    • Creating objects
    • Object properties
    • Object methods
  • Constructor
  • __init__() method
  • self keyword
  • Instance variables
  • Class variables
  • Inheritance
    • Single inheritance
    • Multiple inheritance
    • Multilevel inheritance
    • Hierarchical inheritance
    • Hybrid inheritance
  • super() function
  • Polymorphism
    • Method overriding
    • Duck typing
    • Operator overloading
  • Encapsulation
    • Public members
    • Protected convention
    • Private members
    • Getter and Setter methods
  • Data Abstraction
    • Abstract classes
    • ABC module
    • abstractmethod
    • Abstract methods
    • Real-time abstraction examples
  • Magic methods
  • __str__()
  • __repr__()
  • __len__()
  • __add__()
  • Real-time OOP applications
16Python API Development
  • What is an API?
  • Why APIs are used
  • Client and Server architecture
  • Introduction to REST APIs
  • HTTP protocol basics
  • HTTP request and response
  • HTTP methods
  • GET
  • POST
  • PUT
  • PATCH
  • DELETE
  • HTTP status codes
  • Request headers
  • Response headers
  • JSON data
  • Python requests library
  • Calling external APIs
  • Sending query parameters
  • Sending request body
  • Working with API responses
  • API error handling
  • Introduction to FastAPI
  • Creating FastAPI applications
  • Creating API endpoints
  • Path parameters
  • Query parameters
  • Request body
  • Response models
  • API validation
  • API documentation
  • Building a real-time REST API
17Git & GitHub for AI Engineers
  • Introduction to Version Control
  • What is Git?
  • Why Git is important for AI Engineers
  • What is GitHub?
  • Git vs GitHub
  • Installing Git
  • Git configuration
  • Creating a repository
  • git init
  • git status
  • git add
  • git commit
  • git log
  • Working with branches
  • Creating branches
  • Switching branches
  • Merging branches
  • Resolving merge conflicts
  • GitHub repository creation
  • Push code to GitHub
  • Pull code from GitHub
  • Clone repositories
  • Forking repositories
  • Pull requests
  • Issues
  • README files
  • .gitignore
  • Managing Python project dependencies
  • Versioning AI and Python projects
  • Collaborating with development teams
  • Maintaining AI project repositories

Generative AI & Agentic AI Syllabus

01Module 1: AI & Generative AI Fundamentals

Introduction to Artificial Intelligence

  • What is Artificial Intelligence?
  • History and Evolution of AI
  • Types of Artificial Intelligence
  • Narrow AI vs General AI
  • Real-World Applications of AI
  • AI in Business and Industry
  • Future Scope of Artificial Intelligence

Machine Learning vs Deep Learning vs Generative AI

  • Introduction to Machine Learning
  • Supervised, Unsupervised and Reinforcement Learning
  • Introduction to Deep Learning
  • Neural Networks Fundamentals
  • Deep Learning vs Machine Learning
  • Introduction to Generative AI
  • Generative AI vs Traditional AI
  • Machine Learning vs Deep Learning vs Generative AI
  • Real-World Use Cases

Generative AI Fundamentals

  • What is Generative AI?
  • How Generative AI Works
  • Generative AI Architecture Overview
  • Text Generation
  • Image Generation
  • Audio and Video Generation
  • Code Generation
  • Generative AI Use Cases
  • Limitations and Challenges of Generative AI

Large Language Models (LLMs)

  • What is an LLM?
  • Evolution of Language Models
  • Transformer Architecture Overview
  • Encoder and Decoder Concepts
  • Attention Mechanism
  • Self-Attention and Multi-Head Attention
  • Pre-training and Fine-tuning
  • Instruction-Tuned Models
  • Open-Source vs Closed-Source LLMs

How LLMs Work

  • LLM Training Process
  • Pre-training Data
  • Model Parameters
  • Weights and Biases
  • Inference Process
  • Next Token Prediction
  • Temperature
  • Top-K and Top-P Sampling
  • Model Context and Memory
  • Hallucinations and Their Causes

Tokens & Context Window

  • What are Tokens?
  • Text-to-Token Conversion
  • Tokenization Techniques
  • Tokens vs Words
  • Input and Output Tokens
  • Context Window Explained
  • Context Window Limitations
  • Managing Long Context
  • Token Usage and Cost Optimization

Popular AI Models

  • Overview of Modern LLMs
  • OpenAI Models
  • Google Gemini Models
  • Anthropic Claude Models
  • Meta Llama Models
  • Mistral Models
  • Open-Source LLM Ecosystem
  • Comparing AI Models
  • Selecting the Right Model for a Project

AI APIs & Model Integration

  • What is an AI API?
  • API Keys and Authentication
  • Calling LLM APIs with Python
  • Request and Response Structure
  • System, User and Assistant Messages
  • Prompt and Parameter Configuration
  • Handling API Responses
  • Error Handling and Rate Limits
  • Token and Cost Management
  • Building a Simple AI Application
  • Integrating LLMs into Real-World Applications
02Module 2: Prompt Engineering

Prompt Engineering Fundamentals

  • What is Prompt Engineering?
  • Importance of Prompt Engineering in Generative AI
  • Anatomy of an Effective Prompt
  • Instructions, Context, Input and Output
  • Prompt Clarity and Specificity
  • Common Prompting Mistakes
  • Prompt Engineering Best Practices

Zero-Shot & Few-Shot Prompting

  • What is Zero-Shot Prompting?
  • Zero-Shot Classification and Generation
  • What is Few-Shot Prompting?
  • Providing Examples to LLMs
  • Choosing Effective Examples
  • Few-Shot Classification
  • Zero-Shot vs Few-Shot Prompting
  • Practical Use Cases

Role-Based Prompting

  • Understanding AI Roles and Personas
  • System Instructions and Role Definition
  • Creating Expert AI Personas
  • Role-Based Prompt Design
  • Domain-Specific AI Assistants
  • Developer, Analyst and Consultant Personas
  • Practical Role-Based Prompting Examples

Chain-of-Thought Concepts

  • Introduction to Step-by-Step Reasoning
  • Why Reasoning Matters in Complex Tasks
  • Reasoning-Oriented Prompt Design
  • Decomposing Complex Problems
  • Task Planning and Sequential Instructions
  • Reasoning Limitations and Reliability
  • Practical Problem-Solving Applications

Structured Prompts

  • What are Structured Prompts?
  • Defining Context and Constraints
  • Instruction-Based Prompt Structure
  • Using Delimiters Effectively
  • Providing Input and Expected Output
  • Multi-Step Prompt Structures
  • Reusable Prompt Design Patterns

Output Formatting

  • Controlling AI Output Format
  • Plain Text and Markdown Outputs
  • JSON Output
  • Tables and Lists
  • Structured Data Generation
  • Schema-Based Outputs
  • Validating AI-Generated Outputs
  • Handling Invalid or Unexpected Responses

Prompt Templates

  • What are Prompt Templates?
  • Static vs Dynamic Prompts
  • Variables and Placeholders
  • Reusable Prompt Templates
  • Parameterized Prompts
  • Prompt Templates with Python
  • Templates for AI Applications
  • Building a Reusable Prompt Library

Prompt Optimization

  • What is Prompt Optimization?
  • Improving Prompt Accuracy
  • Reducing Ambiguity
  • Reducing Hallucinations
  • Optimizing Prompt Length
  • Improving Response Consistency
  • Token and Cost Optimization
  • Prompt Testing and Evaluation
  • Iterative Prompt Improvement
  • Real-World Prompt Engineering Projects
03Module 3: LLM Application Development

Calling LLM APIs

  • Introduction to LLM APIs
  • API Keys and Authentication
  • Making API Requests with Python
  • Request and Response Parameters
  • Temperature, Token Limits and Model Parameters
  • Error Handling and Rate Limits

Chat Completions

  • Understanding Chat-Based LLMs
  • Chat Completion API Structure
  • Conversation-Based Applications
  • Managing Conversation History
  • Multi-Turn Conversations

System & User Messages

  • System Messages
  • User Messages
  • Assistant Messages
  • Message Roles and Priorities
  • Designing Effective Instructions
  • Managing Context Across Conversations

Streaming Responses

  • What is Streaming?
  • Streaming vs Non-Streaming Responses
  • Token-by-Token Generation
  • Implementing Streaming with Python
  • Building Real-Time AI Interfaces

Structured Outputs

  • Introduction to Structured Outputs
  • Schema-Based Responses
  • JSON Schema
  • Structured Data Extraction
  • Validating Model Responses

Function Calling

  • What is Function Calling?
  • Defining Functions for LLMs
  • Function Parameters and Schemas
  • Calling External Functions
  • Handling Function Results
  • Building Function-Calling Applications

JSON Responses

  • Generating Valid JSON
  • JSON Schema Validation
  • Parsing JSON with Python
  • Handling Invalid JSON
  • Using JSON in Backend Applications

Building LLM Applications

  • LLM Application Architecture
  • Backend Integration
  • Building AI Chat Applications
  • Environment and Configuration Management
  • Mini Project: LLM-Powered Application
04Module 4: Embeddings & Vector Databases

What are Embeddings?

  • Introduction to Embeddings
  • Text Representation as Vectors
  • How Embedding Models Work
  • Embedding Dimensions
  • Embedding Use Cases

Text Embeddings

  • Generating Text Embeddings
  • Embedding Models
  • Document Embeddings
  • Query Embeddings
  • Comparing Embedding Models

Semantic Search

  • Keyword Search vs Semantic Search
  • Understanding Semantic Similarity
  • Query-to-Document Matching
  • Semantic Search Applications

Vector Databases

  • What is a Vector Database?
  • Why Vector Databases are Used in AI
  • Vector Storage and Indexing
  • Popular Vector Database Technologies
  • Choosing a Vector Database

Similarity Search

  • Cosine Similarity
  • Euclidean Distance
  • Dot Product
  • Nearest Neighbor Search
  • Top-K Retrieval

Metadata & Filtering

  • Understanding Metadata
  • Adding Metadata to Documents
  • Metadata-Based Filtering
  • Combining Filtering with Similarity Search

Vector Database Integration

  • Connecting Python Applications
  • Creating Collections and Indexes
  • Insert and Update Vectors
  • Querying Vector Databases
  • Building a Semantic Search Application
05Module 5: RAG – Retrieval Augmented Generation

What is RAG?

  • Introduction to Retrieval-Augmented Generation
  • Why RAG is Needed
  • RAG vs Traditional LLM Applications
  • RAG Use Cases

RAG Architecture

  • RAG Components
  • Ingestion Pipeline
  • Retrieval Pipeline
  • Generation Pipeline
  • End-to-End RAG Architecture

Document Loading

  • Loading PDF Documents
  • Loading Text Files
  • Loading Word Documents
  • Loading Web Pages
  • Loading Multiple Documents

Document Splitting

  • Why Documents Need Splitting
  • Character-Based Splitting
  • Recursive Splitting
  • Sentence-Based Splitting
  • Document Structure Preservation

Chunking Strategies

  • Chunk Size
  • Chunk Overlap
  • Fixed-Size Chunking
  • Semantic Chunking
  • Choosing the Right Chunking Strategy

Embeddings & Retrieval

  • Creating Document Embeddings
  • Creating Query Embeddings
  • Storing Embeddings
  • Retrieving Relevant Documents

Vector Search

  • Similarity-Based Retrieval
  • Top-K Search
  • Similarity Thresholds
  • Filtering Retrieved Results

Context Retrieval

  • Building Retrieval Context
  • Context Selection
  • Context Ordering
  • Managing Context Window

RAG Pipeline

  • Query Processing
  • Retrieval
  • Context Construction
  • Prompt Generation
  • LLM Response Generation
  • End-to-End RAG Implementation

RAG with Multiple Documents

  • Multi-Document Ingestion
  • Document Metadata
  • Cross-Document Retrieval
  • Building a Multi-Document Q&A System
06Module 6: Advanced RAG

Semantic Search

  • Advanced Semantic Retrieval
  • Query and Document Similarity
  • Improving Retrieval Quality

Hybrid Search

  • Keyword Search
  • Vector Search
  • Combining Keyword and Vector Search
  • Hybrid Retrieval Strategies

Metadata Filtering

  • Metadata Design
  • Filtering by Source
  • Filtering by Date and Category
  • Combining Metadata and Semantic Search

Query Transformation

  • Query Rewriting
  • Query Expansion
  • Multi-Query Retrieval
  • Breaking Complex Queries into Sub-Queries

Re-Ranking

  • What is Re-Ranking?
  • Initial Retrieval vs Re-Ranking
  • Improving Top-K Results
  • Re-Ranking Strategies

Context Optimization

  • Reducing Irrelevant Context
  • Context Compression
  • Context Ordering
  • Managing Long Context
  • Optimizing Token Usage

RAG Evaluation

  • Retrieval Evaluation
  • Answer Quality Evaluation
  • Faithfulness
  • Relevance
  • Building RAG Evaluation Datasets

Reducing Hallucinations

  • Sources of RAG Hallucinations
  • Grounding Responses in Retrieved Context
  • Prompt-Based Techniques
  • Retrieval Quality Improvements
  • Confidence and Fallback Strategies

Production RAG Architecture

  • Scalable RAG Architecture
  • Data Ingestion Pipelines
  • Vector Database Management
  • Caching
  • Monitoring and Logging
  • Production RAG Best Practices
07Module 7: LangChain

LangChain Fundamentals

  • Introduction to LangChain
  • LangChain Architecture
  • LangChain Components
  • Setting Up a LangChain Project

Models

  • Chat Models
  • LLM Integration
  • Model Parameters
  • Model Selection

Prompts

  • Prompt Templates
  • Chat Prompt Templates
  • Dynamic Prompt Variables
  • Reusable Prompts

Output Parsers

  • Understanding Output Parsers
  • Text Output Parsing
  • Structured Output Parsing
  • JSON Output Parsing

Chains

  • What are Chains?
  • Sequential Workflows
  • Runnable Components
  • Building Multi-Step LLM Pipelines

Document Loaders & Text Splitters

  • Document Loading
  • PDF and Web Loaders
  • Text Splitting
  • Chunking Strategies

Retrievers

  • What is a Retriever?
  • Vector Store Retrievers
  • Similarity Retrieval
  • Custom Retrieval Strategies

LangChain RAG Applications

  • Building a RAG Pipeline
  • Connecting Documents to LLMs
  • Retriever Integration
  • Building Document Q&A Applications

Building LLM Applications

  • Application Architecture
  • Combining LangChain Components
  • Building Production-Ready Applications
  • LangChain Project
08Module 8: AI Agents Fundamentals

What is an AI Agent?

  • Introduction to AI Agents
  • Agent Components
  • LLM as an Agent Brain
  • Tools, Memory and Actions

AI Agent vs Chatbot

  • Traditional Chatbots
  • LLM-Based Chatbots
  • AI Agents
  • Key Differences and Use Cases

AI Agent vs RAG

  • RAG-Based Applications
  • Agent-Based Applications
  • Combining RAG and Agents
  • Choosing the Right Architecture

Agent Architecture

  • Agent Components
  • Planning
  • Memory
  • Tools
  • Reasoning and Action

Agentic Workflows

  • Sequential Workflows
  • Conditional Workflows
  • Iterative Workflows
  • Event-Driven Workflows

Planning & Decision Making

  • Task Decomposition
  • Planning Strategies
  • Decision Making
  • Tool Selection
  • Goal-Oriented Execution

Agent Loop

  • Observe
  • Think/Plan
  • Act
  • Observe Results
  • Iterative Execution
  • Stopping Conditions

Autonomous AI Systems

  • Autonomy Levels
  • Human Oversight
  • Autonomous Decision Making
  • Agent Reliability

Real-World AI Agent Use Cases

  • Customer Support Agents
  • Research Agents
  • Data Analysis Agents
  • Developer Agents
  • Business Automation Agents
09Module 9: AI Agents & Tools

Agent Tools

  • What are Agent Tools?
  • Tool Design Principles
  • Tool Inputs and Outputs

Tool Calling & Function Calling

  • Tool Calling Concepts
  • Function Schemas
  • Tool Selection by LLMs
  • Handling Tool Results

Custom Tools

  • Creating Custom Python Tools
  • Tool Parameters
  • Tool Validation
  • Tool Error Handling

API Tools

  • Calling REST APIs
  • Authentication
  • API Response Processing
  • Building API-Based Tools

Search Tools

  • Web Search Tools
  • Document Search
  • Knowledge Retrieval
  • Search Result Processing

Database Tools

  • Database Connectivity
  • SQL-Based Tools
  • Query Generation
  • Safe Database Operations

Tool Selection & Execution

  • Choosing the Right Tool
  • Tool Execution Flow
  • Sequential Tool Calls
  • Multiple Tool Calls
  • Handling Tool Failures

Building Tool-Using Agents

  • Designing Tool-Based Agents
  • Connecting Multiple Tools
  • Agent + RAG + Tools
  • Building an AI Automation Agent
10Module 10: LangGraph

Introduction to LangGraph

  • What is LangGraph?
  • Why LangGraph for Agentic AI?
  • LangGraph vs Traditional Chains

Graph Architecture

  • Graph-Based AI Workflows
  • Nodes
  • Edges
  • State
  • Graph Execution

Nodes & Edges

  • Creating Nodes
  • Connecting Nodes
  • Sequential Execution
  • Conditional Edges

State Management

  • Understanding Agent State
  • State Schema
  • Updating State
  • Managing Conversation State

Conditional Routing

  • Routing Logic
  • Decision Nodes
  • Dynamic Workflows
  • Conditional Agent Execution

Agent Workflows

  • Building Agent Graphs
  • Tool-Calling Workflows
  • Multi-Step Agent Execution
  • RAG + Agent Workflows

Checkpoints & Persistent Workflows

  • Checkpoint Concepts
  • Saving Agent State
  • Resuming Workflows
  • Long-Running Agent Tasks

Human-in-the-Loop

  • Human Approval Workflows
  • Pausing Agent Execution
  • Review and Resume
  • Human Oversight

Building LangGraph Agents

  • Complete Agent Architecture
  • Agent + Tools + RAG
  • Stateful Agents
  • End-to-End LangGraph Project
11Module 11: Model Context Protocol (MCP)

What is MCP?

  • Introduction to Model Context Protocol
  • Why MCP is Important for AI Agents
  • MCP Use Cases

MCP Architecture

  • MCP Architecture Overview
  • Host, Client and Server Concepts
  • Communication Flow
  • Protocol-Based Tool Integration

MCP Client

  • Understanding MCP Clients
  • Connecting to MCP Servers
  • Discovering Available Capabilities
  • Managing MCP Connections

MCP Server

  • Understanding MCP Servers
  • Setting Up an MCP Server
  • Server Configuration
  • Exposing AI Capabilities

MCP Tools

  • Creating MCP Tools
  • Tool Schemas
  • Tool Inputs and Outputs
  • Connecting External Services

MCP Resources

  • Understanding MCP Resources
  • Exposing Data Through Resources
  • Resource Access and Management

MCP Prompts

  • Reusable MCP Prompts
  • Prompt Templates
  • Dynamic Prompt Arguments

Connecting Agents with MCP

  • Agent and MCP Integration
  • Using MCP Tools from Agents
  • Connecting Multiple MCP Servers
  • Agentic Workflows with MCP

Building MCP-Based Applications

  • Building an MCP Server
  • Building an MCP Client
  • Connecting Agents and MCP
  • End-to-End MCP Project
12Module 12: Multi-Agent Systems

Single Agent vs Multi-Agent

  • Single-Agent Architecture
  • Multi-Agent Architecture
  • When to Use Multi-Agent Systems

Multi-Agent Architecture

  • Centralized Architecture
  • Decentralized Architecture
  • Hierarchical Architecture
  • Agent Coordination

Specialized Agents

  • Research Agent
  • Data Analysis Agent
  • Coding Agent
  • Content Agent
  • Domain-Specific Agents

Supervisor Agent

  • Role of Supervisor Agent
  • Task Routing
  • Agent Selection
  • Workflow Management

Agent Handoffs

  • Handoff Concepts
  • Passing Context Between Agents
  • Conditional Handoffs
  • Managing Agent Transitions

Agent-to-Agent Communication

  • Agent Communication Patterns
  • Message Passing
  • Shared State
  • Context Management

Task Delegation

  • Task Decomposition
  • Assigning Tasks to Agents
  • Dependency Management
  • Result Aggregation

Parallel Agent Workflows

  • Parallel Task Execution
  • Concurrent Agents
  • Combining Parallel Results
  • Error Handling in Parallel Workflows

Multi-Agent Coordination

  • Agent Coordination Strategies
  • Conflict Resolution
  • Shared Memory
  • Multi-Agent Decision Making
  • End-to-End Multi-Agent Project
13Module 13: Production AI Applications

AI Application Architecture

  • Production AI Architecture
  • Frontend and Backend Integration
  • LLM Service Layer
  • Database and Vector Database Integration
  • Scalable AI Application Design

Agent Evaluation

  • Why Agent Evaluation Matters
  • Evaluating Agent Responses
  • Tool Selection Evaluation
  • Task Completion Metrics
  • Building Evaluation Datasets

Tracing & Observability

  • AI Application Logging
  • LLM Request and Response Tracing
  • Agent Execution Tracing
  • Monitoring Latency and Errors
  • Observability Best Practices

Error Handling

  • API Failures
  • Model Errors
  • Tool Failures
  • Timeout Handling
  • Retry Strategies
  • Fallback Models and Responses

Guardrails

  • What are AI Guardrails?
  • Input Guardrails
  • Output Guardrails
  • Content Validation
  • Safety and Policy Controls

Prompt Injection

  • What is Prompt Injection?
  • Direct and Indirect Prompt Injection
  • Prompt Injection Risks in RAG
  • Prompt Injection in AI Agents
  • Defensive Strategies

AI Security Basics

  • API Key Security
  • Data Privacy
  • Access Control
  • Secure Tool Execution
  • Data Leakage Prevention
  • AI Application Security Best Practices

Cost Optimization

  • Understanding LLM Costs
  • Token Optimization
  • Model Selection for Cost Efficiency
  • Caching Strategies
  • Reducing Unnecessary LLM Calls

API & Model Management

  • Managing Multiple AI Models
  • API Configuration
  • Environment Variables
  • Model Fallback Strategies
  • Version Management

Production Deployment

  • Preparing AI Applications for Production
  • Backend Deployment
  • Environment Configuration
  • Database Deployment
  • Containerization Basics
  • Scaling AI Applications
  • Production Monitoring
  • End-to-End Production AI Project
Real-time Projects

Six projects that put RAG, agents and prompt engineering into practice.

Project 1

Health Assistant

Analyzes patient data to help hospitals and clinics identify and diagnose diseases earlier.

Project 2

Jarvis Assistant

A personal and business assistant for organization, customer service and daily tasks.

Project 3

Shopping Guide

Uses prompt engineering to give product recommendations and help users decide what to buy.

Project 4

Order Bot

A conversational ordering system connected to backend services.

Project 5

PDF Content Reader

Reads documents in different formats and answers questions about their content.

Project 6

Text to Image Generator

Turns written descriptions into images using prompt engineering.

What we provide

Everything you need to learn, practise and get placed, in one program.

✦ TOOLS YOU WILL MASTER

Learn Industry-Standard Tools

Python

Programming

DeepSeek

Open LLMs

OpenAI

LLM APIs

Claude

LLM APIs

LangChain

LLM Framework

Ollama

Local Models

Practical learning with real-world applications

Jobs & salary after FullStack AI with GenAI & Agentic AI

The AI roles this course prepares you for, what they typically pay in India, and how pay grows with experience.

₹6–12 LPACommonly reported GenAI fresher range
₹12–22 LPAGenAI freshers with a strong portfolio
₹60–80+ LPAArchitect or principal level, 10+ years

Roles this course prepares you for

Typical annual pay in India (LPA = lakhs per annum). Filter by skill or sort by pay.

Skill:
Sort:
Role Typical pay Range (₹0–40L) Key course skills
AI Engineer (fresher) ₹6–9 LPA
Python, LLM APIs, FastAPI
Generative AI Engineer (0–2 yrs) ₹6–12 LPA
Prompting, LLM APIs, RAG
AI Agent Developer (fresher)* ₹6–12 LPA
Agents, LangGraph, MCP
LLM Engineer (2–5 yrs) ₹18–35 LPA
LLM apps, LangChain, evaluation
RAG Engineer (3–5 yrs) ₹20–40 LPA
Embeddings, vector DBs, RAG

Pay growth with experience

Indicative annual pay range for AI and GenAI roles. Click a column to see details.

Monthly figures are gross CTC divided by 12. In-hand pay is lower after tax and deductions.

Please note: figures are indicative annual CTC before tax, taken from public 2026 India AI salary guides that differ from each other. *No separate figure was found for AI Agent Developer, so the general GenAI fresher range is shown. Actual offers depend on skills, projects, city and employer, and course completion does not guarantee a job.

Book Your Free Demo Class

Book your free demo class and experience our teaching in Telugu from our Industry experts.

Frequently asked questions about FullStack AI with GenAI & Agentic AI

What you learn, how classes run, what it costs and what support you get, answered before you enroll.

Your questions

Search or filter by topic.

Topic:
What will I learn in this course?

You learn Generative AI, Agentic AI, AI agents, RAG, LangChain, LangGraph and MCP from basic to advanced level, starting with the Python and API skills you need.

Which language are the classes taught in?

Classes are taught in Telugu.

Why choose PythonLife?

We teach industry-focused skills from scratch to advanced level, and an experienced instructor is available to clear your doubts.

What is the course fee?

The course fee is ₹7, 999. You can pay on our site with the Enroll now button.

When does the batch start and what are the timings?

The batch starts on 12th November 2026. Live classes run Monday to Saturday, 7:00 PM to 9:00 PM.

Will I get recordings if I miss a class?

Yes. All sessions are recorded and shared with students.

If I don' t like the course, can I get a refund?

No. Fees are not refundable, so we recommend watching the demo class first.

How do I enroll?

Pay from our site. Right after payment you receive a confirmation from us with the next steps.

Is this course for me if I am not from a computer science background?

Yes. All concepts are taught from basic to advanced level, and the instructor checks that you understand before moving on.

What are the eligibility criteria?

There are none. The course is taught from start to finish, so anyone interested can join.

Will I get job opportunities as a fresher?

We train you to industry requirements and run mock interviews. The course includes a 2+month internship, 100% placement assistance and 20+daily job updates.

Which roles can I prepare for?

AI Engineer, Generative AI Engineer, AI Agent Developer, RAG Engineer and LLM Engineer.

What projects will I build?

You build real-time projects including a Health Assistant, Jarvis Assistant, Shopping Guide, Order Bot, PDF Content Reader and Text to Image Generator.

Will I get a certificate?

Yes. You receive a course completion certificate from PythonLife when you submit your project at the end of the course.

Which tools and models are covered?

OpenAI, Claude, DeepSeek, LangChain and Ollama, along with LangGraph, vector databases and MCP.

Still have a question?

Message us about batch details, fees or the syllabus.

Chat on WhatsAppContact usEnroll now

PythonLife is a Telugu-first EdTech platform dedicated to making technology learning simple, practical, and career-focused. Since 2020, we have helped students and professionals learn in-demand technologies including Python, Data Science, AI, Machine Learning, Generative AI, Agentic AI, DevOps, SQL, Java, and Power BI through practical, industry-oriented training.

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