Description

Teach developers the essential skills needed to add Generative AI (GenAI) features into their applications, including chatbots, content generation, and predictive analysis. This Generative AI course teaches learners how to build, deploy, and scale GenAI applications. Hands-on labs address practical challenges and solutions, offering a solid foundation in this evolving field.

Skills Gained

By the end of this course, participants will be able to:

Enhance customer interactions with generative AI
Automate tasks with LLM-based applications
Streamline workflows using LangChain
Improve performance with generative AI evaluation metrics
Support business growth with GenAI deployment and scaling strategies

Who Can Benefit

Software Developers
Data Scientists & Analysts

Course Overview

Understanding Generative AI

What is Intelligence?
Mechanisms of Intelligence
What is Artificial Intelligence?
How does AI work?
What can AI do?
What is Generative AI?
How does a Large Language Model work?

Basics of Prompt Engineering

Basics of Prompting
What is Prompt Engineering?
Why It’s Both Art and Science
Communicating with Large Language Models
Why is Prompt Engineering Useful?

Building Generative AI Applications

Common Applications of Generative AI
Deployment Considerations
Accessing Large Language Models via API
Utilizing LLM APIs in Applications
Enhancing User Interactions
Producing Structured Outputs
Putting it Together: Building GenAI Applications

Accelerating Development With Langchain

What is LangChain?
LangChain Framework Overview
Benefits of LangChain
LangChain Terminology and Concepts
Language Models
Creating Tools
Common LangChain Use Cases

Evaluating Generative AI Applications

Introduction to Evaluation
Key Evaluation Attributes
Common Metrics for Generative AI
Interpreting Evaluation Results
LLM Evaluation Pipeline
Balancing Metrics and Human Judgment

Deploying and Scaling Generative AI Applications

Unique Challenges in Deployment
Business Models: IaaS, PaaS, SaaS
Cloud vs. On-Premise Deployments
Managing Latency and Throughput
Ensuring Data Privacy and Security
Monitoring and Auto-Scaling

Basics of Generative AI Monitoring

Differences Between Evaluation and Monitoring
Comparison: Evaluation vs. Monitoring
Understanding the Monitoring Workflow
Alerts, Logs, and Verification
Setting Up a Monitoring System
Drift Detection Methods

Debugging and Testing Generative AI Applications

Debugging Generative AI Applications
Workflow for Testing Chatbot
Systematic Testing Framework
Best Practices in Debugging and Testing
CI/CD for LLMs
Ethical Considerations in LLM Development

Introduction to Agentic AI

Defining Agentic AI
Components of Agentic AI
Characteristics of Agentic AI
Agentic AI vs. Generative AI
Benefits and Challenges of Agentic AI
Real-world Applications of Agentic AI

Generative AI Use Cases and Ethics

Introduction to Generative AI Use Cases and Ethics
Real-world Applications of Generative AI
Identifying GenAI Use Cases
Ethical Considerations in Generative AI
Responsible Use of Generative AI
Case Studies and Examples

Building Generative AI Applications Virtual Classroom Live November 18, 2026

Price: $3,000

Enroll today to reserve your spot!