Generative AI

Self-paced recorded course

Build Python applications with language models. Learn to retrieve answers from documents with RAG, give LangGraph agents memory and tools, and deploy applications on AWS while tracking costs.

Complete recorded course

₹9,600 INR / $100 USD

Pay in INR or USD. Our team will help with payment and recording access.

1 year of recording access. No live classes, labs or certification exam included.

Course content

Python Basics

  • Python Installation & Environment Setup (Anaconda / venv)
  • Variables, Data Types (str, int, float, bool, list, tuple, dict, set)
  • Basic Operators and Expressions
  • Control Flow: if-else, for, while loops
  • Functions & Return Values
  • Write a Python script to take a sentence and count words, characters, and vowels.

Working with Libraries

  • Installing and Importing Libraries (pip, conda)
  • Using Popular Libraries (numpy, pandas, matplotlib)
  • Reading Documentation & Examples
  • Install numpy and pandas, create a DataFrame from a list of dictionaries, anddisplay basic statistics.

File Handling & Data Processing

  • Reading & Writing Text Files
  • Working with CSV and JSON
  • Handling Exceptions (try-except)
  • Read a CSV file of text prompts and write the first 10 prompts to a new text file.

Introduction to Generative AI

  • What is Generative AI?
  • Use Cases of Generative AI
  • Understanding Tokenization
  • Understanding Vector Databases
  • Using a tokenizer

System Prompts for Generative AI

  • Understanding Zero Shot Prompting
  • Interacting with OpenAI using Zero Shot Prompting
  • Understanding Few Shot Prompting
  • Interacting with OpenAI using Few Shot Prompting
  • Understanding Chain-of-Thought Prompting
  • Interacting with OpenAI using CoT Prompting
  • Understanding Persona Based Prompting
  • Interacting with OpenAI using Persona Based Prompting

Combining LLMs with Custom Scripts

  • Understanding Custom Scripting for LLMs
  • Advantages & Disadvantages of Scripting with LLMs
  • Writing Scripts for LLMs
  • Connecting OpenAI to Custom Scripts

Retrieval-Augmented Generation (RAG)

  • Understanding the Need of RAG
  • Understanding RAG
  • Understanding Qdrant Vector DB
  • Understanding Langchain
  • Creating a RAG Pipeline Project
  • Deploy RAG on Kubernetes using a Microservice architecture

Retrieval-Augmented Generation in Enterprises

  • Understanding RAG issues in Scalability
  • Discussing Possible RAG Solutions in Enterprise
  • Deploying Async RAG Using a Queue Service

LangGraph for Agentic AI

  • Introduction to LangGraph
  • LangGraph vs N8N Comparsion
  • Understandings Nodes & Edges for Agentic AI
  • Creating a Simple Agentic AI
  • Creating a Multi Step Agentic AI

Advanced LangGraph for Agentic AI

  • Understanding Agentic AI Memory Issues
  • Understanding Checkpointing in Agentic AI
  • Using MongoDB for Checkpointing
  • Creating Agentic AI Workflow with MongoDB Checkpointing

Memory for Customer Facing AI

  • Understanding Contextual Limitations
  • Understanding how Recommendations Systems Work
  • Need of Memory in Customer Facing Apps
  • Deploying AI Chatbot with Neo4J

Model Context Protocol (MCP)

  • Understanding how AI Applications Communicate
  • Understanding MCP Benefits
  • Creating an MCP Server
  • Creating an MCP Client

AI Voice Agents & Enterprise Usage

  • Understanding AI Voice Usage Scenarios
  • Understanding Text-to-Speech (TTS)
  • Understanding Speech-to-Text ( STT)
  • Creating a meetings Note Taker AI Software

AI GuardRails and Cost Tracking

  • Understanding the need of GuardRails in AI Prompts
  • Creating manual Guard Rails using Regular Expressions in Python
  • Using LLM as a judge via Guard Rails Hub
  • Understanding Cost Tracking Requirements for LLM calls
  • Visualising Token Usage and Costs using Prometheus and Grafana

AI Deployments on AWS

  • Understanding AWS AI Services
  • Comparing Python RAG Deployment with AWS Bedrock
  • Deploying a RAG App on AWS using Bedrock
  • Testing AI RAG Application using AWS Console
  • Testing AI RAG Application using AWS Python SDK

Cost Optimization for Generative AI Apps

  • Understanding Caching in Generative AI
  • Understanding Direct Hit Cache Limitations
  • Deploying Redis with Generative AI For Direct Hit Cache
  • Deploying Redis and Vector Databases with Generative AI for Score Based Sematic Caching

Good to know

How does this course work?
This is a self-paced recorded course. Watch the lessons on your own schedule and revisit topics as you learn. There are no live classes or scheduled batches for this course.
What is included?
The course recordings are included, with 1 year of recording access. Lab environments, live teaching and certification exams are not included.
Can I watch a lesson before buying?
Yes. Choose a free preview in the course content to watch a sample lesson. The rest of the course is available after purchase.
What happens when I click Buy course?
A short enquiry form opens. Share your details and our team will contact you to confirm the price, payment and recording access. Submitting the form does not take a payment or activate course access.

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