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Applied Generative AI and Agentic AI

Applied Generative AI and Agentic AI

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Application closes 23rd Apr 2026

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PROGRAM OUTCOMES

Build and deploy AI solutions

Leverage GenAI and Agentic AI to solve business challenges and drive innovation

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    Build real AI tools, chatbots, classifiers, and summarizers, using Python and modern AI APIs

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    Understand how LLMs, GenAI, and Agents work to make informed decisions

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    Write, fix, and improve Python script using AI tools, focused on getting things working, not memorizing syntax

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    Write reliable prompts and build systems where AI takes actions, uses tools, and coordinates across agents

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    Select between prompting, RAG, and fine-tuning for a given problem, not just default to whatever's trending

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    Identify where bias, hallucination, and legal exposure arise in AI systems, and apply steps to reduce them

Earn a Certificate of Completion from Johns Hopkins University

  • #7 National University Rankings

    #7 National University Rankings

    U.S. News & World Report, 2026

  • #2 Computer Information Technology

    #2 Computer Information Technology

    U.S. News & World Report, 2026

  • #14 Best Global University

    #14 Best Global University

    U.S. News & World Report, 2026

  • #1 Biomedical Engineering Program

    #1 Biomedical Engineering Program

    US News and World Report, 2026

Key program highlights

Why choose this program?

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    Faculty and expert-led live masterclasses

    Participate in monthly live sessions led by Johns Hopkins University faculty and industry experts, which offer the latest insights and practical guidance on AI strategy.

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    Weekly mentored learning sessions

    Participate in interactive, mentor-led sessions where industry experts present case studies and provide deep insights into AI applications in business.

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    Generative and Agentic AI applications

    Build application-ready skills in Agentic and Generative AI. Learn to design automated workflows and develop AI applications that address real business needs using the OpenAI API.

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    Hands-on learning

    Build practical skills through 3 hands-on projects and real-world case studies, supported by access to OpenAI API Keys from Great Learning

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    Earn a Certificate of Completion from JHU

    Upon successful completion, earn a prestigious certificate and 11 CEUs from Johns Hopkins University, recognizing your proficiency in Generative AI

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    Dedicated program support

    Access to academic learning support, a dedicated program manager, and peer groups through discussion forums for a comprehensive learning experience.

Skills you will learn

Prompt Engineering

Building Generative AI Workflows

Python for Artificial Intelligence

Ethical AI Practices

Multimodal AI Foundation Models

Evaluating Generative AI Solutions

Fine-tuning LLMs

Agentic AI Development

Secure AI Development

Contrastive Learning

Cross-Modal Alignment

Natural Language Processing

Prompt Engineering

Building Generative AI Workflows

Python for Artificial Intelligence

Ethical AI Practices

Multimodal AI Foundation Models

Evaluating Generative AI Solutions

Fine-tuning LLMs

Agentic AI Development

Secure AI Development

Contrastive Learning

Cross-Modal Alignment

Natural Language Processing

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  • Overview
  • Learning Path
  • Curriculum
  • Projects
  • Tools
  • Certificate
  • Faculty
  • Mentors
  • Career Support
  • Fees
  • FAQ
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This program is ideal for

Individuals looking to explore how Generative AI can solve real-world business problems

  • Technology Professionals

    Who want to learn and apply Generative AI, enabling them to build and deploy AI-driven solutions at work or for personal projects

  • Data Professionals

    Who want to use GenAI to work smarter, extracting deeper insights, analyzing data more effectively

  • Technology Consultants and Technical Managers

    Seeking to understand GenAI, apply best practices, manage risks, and guide technical teams in developing AI solutions

  • STEM Graduates

    Who wish to upskill through hands-on training in Generative AI and become part of a cutting-edge industry with significant growth potential

Experience a unique learning journey

  • Learn from world-renowned faculty

    Learn critical concepts through live masterclasses and recorded video lectures by JHU faculty

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  • Engage with your mentors

    Clarify your doubts and gain practical skills during weekly live mentorship sessions with industry experts

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  • Work on hands-on projects

    Work on projects to apply the concepts & tools learnt in the module

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  • Get personalized assistance

    Our dedicated program managers will support you through your learning journey

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Comprehensive Curriculum

The curriculum, designed by the faculty of Johns Hopkins University, Great Learning, and leading industry practitioners, is taught by the best-in-class professors and practicing industry experts. The program aims to acquaint the learners with the skills needed to solve problems and deploy Generative AI solutions for various business applications.

Pre-Work Module

This preparatory module builds a strong AI foundation through key concepts, Generative AI basics, and Python skills. It introduces Machine Learning models, real-world applications, and problem-solving approaches to prepare you for hands-on AI development and collaboration.

Concepts Covered

- Introduction to the World of AI - Overview of Generative AI - Python with GenAI and Learn with AI Mentor - Foundations of AI - Introduction to Python

Module 01 | AI, GenAI, and Agentic AI Foundations

This module explores the evolution of Generative AI and agents, and their real-world applications. It introduces vibe coding for rapid prototyping, using AI tools to build, test, and iterate Python and Machine Learning solutions efficiently while evaluating performance, accuracy, and reliability.

Week 1: Generative AI and Agentic AI Landscap

Understand the history of Generative AI, learn the evolution of AI agents, explore real-world Gen AI use cases across different sectors, and study key industrial applications of agents.

Week 2: AI-Assisted Python Coding

Understand the concept of ai-assisted coding and how it differs from traditional development, set up a rapid Python environment for experimentation, and use AI tools to generate and refine code effectively. Learn to prototype quickly without detailed planning, integrate APIs or datasets, create simple visualizations, and apply generative techniques in coding. Practice debugging and iterating using AI and intuition, and build and present a small end-to-end Python project while evaluating AI-generated code for correctness and limitations.

Week 3: AI-Assisted Coding for ML Workflows

Understand how vibe coding applies to machine learning workflows, set up an environment for rapid ML experimentation, and use AI tools to build ML pipelines and models. Learn to perform quick exploratory data analysis, prepare and preprocess datasets, build baseline models efficiently, and iterate through experimentation. Gain skills in evaluating model performance, debugging issues, automating parts of the ML workflow, and assessing models for bias, accuracy, and reliability.

Week 4: Hands-On Project

Work on a hands-on project building a Personal Finance Coach & Spending Analyzer using industry-relevant AI, Python, and agentic AI tools and technologies.

Week 05 | Learning Break

Learning breaks are structured pauses that allow you to consolidate concepts, complete pending work, and reinforce your understanding before progressing further.

Module 02 | Building and Evaluating GenAI Workflows

This module develops advanced LLM skills through Prompt Engineering, evaluation, and optimization techniques. Learn embeddings, RAG, and fine-tuning to build, improve, and deploy reliable AI applications with strong performance, accuracy, and scalability.

Week 6: Prompt Engineering for NLP Workflows

Understand the concept of prompt engineering and its role in refining LLMs into conversational assistants, and learn the key components of prompting, including templates and parameters. Explore advanced prompting techniques such as few-shot, chain-of-thought, tree-of-thought, self-consistency, and ReAct. Learn how to transform traditional NLP tasks into generative AI problems for more flexible solutions, including sentiment classification, transcription of audio or video into text, and generation of concise summaries. Gain the ability to generate new text outputs from given inputs to enhance a range of NLP applications.

Week 7: Evaluation of GenAI Workflows

Understand how to evaluate prompt stability by measuring consistency across multiple non-deterministic LLM runs and applying prompt versioning strategies. Learn how to assess LLM output quality using metrics such as accuracy, hallucination rates, and confidence scores like log probabilities. Apply programmatic prompt optimization techniques using frameworks such as DSPy to improve performance beyond manual prompting. Design scalable evaluation systems using LLM-as-a-Judge methods and gold-standard datasets with inter-annotator agreement for reliable human evaluation.

Week 8: Retrieval Augmented Generation (RAG) and Advanced RAG

Understand the roles and differences of embeddings and tokenization in large language models (LLMs), and learn how Byte-Pair Encoding manages vocabulary efficiently. Gain insights into sentence embeddings and how they improve contextual understanding in LLMs. Explore how Retrieval-Augmented Generation (RAG) enhances response accuracy and relevance using information retrieval, and understand the underlying algorithms and their impact on performance. Learn to differentiate between simple and advanced RAG implementations, build a basic fine-tuned RAG system, and evaluate RAG models effectively.

Week 9: Fine-Tuning Small Language Models

Understand the role and benefits of small language models and learn when fine-tuning is appropriate versus prompting or RAG. Learn to prepare and structure datasets for fine-tuning tasks and create effective instruction-based training data. Apply basic fine-tuning techniques to small language models using open-source tools and libraries, and run a complete end-to-end training pipeline. Evaluate model performance using appropriate metrics, optimize training for cost and efficiency constraints, and deploy a fine-tuned model in a simple application.

Week 10: Hands-On Project

Work on a hands-on project building a Clinical Decision Support System for hospitals using industry-relevant AI, RAG pipelines, fine-tuned small language models, and advanced evaluation techniques.

Week 11 | Learning Break

Learning breaks are structured pauses that allow you to consolidate concepts, complete pending work, and reinforce your understanding before progressing further.

Module 03 | Building Secure Agentic AI Workflows

This module equips you to design and run AI agents that complete tasks using tools, and to evaluate whether they actually work using clear, structured methods. You’ll understand when to use single vs multi-agent systems and how to coordinate them effectively. It also covers identifying bias and risk, staying within basic AI regulations, and making practical trade-offs to keep systems reliable and cost-efficient.

Week 12: Secure and Responsible GenAI Solutions

Understand how to identify sources of bias and risk in both human and AI systems, apply techniques to mitigate bias and risk, and gain awareness of current laws and regulations governing the responsible use of AI.

Week 13: Building Single Agent Systems

Understand AI memory structures by differentiating between short-term, long-term, episodic, and semantic memory. Explain agentic behavior with a focus on reasoning, planning, and tool calling in autonomous agents. Identify and categorize different types of AI agents based on their capabilities and use cases. Apply ReAct and the Model Context Protocol (MCP) to improve reasoning and external tool integration. Design and deploy autonomous single-agent systems for end-to-end task execu

Week 14: Evaluating Agentic AI Systems

Understand key evaluation dimensions for agentic AI, including task success, reasoning trajectories, tool execution, and system efficiency using DeepEval. Apply evaluation methodologies such as LLM-as-a-Judge, deterministic rule-based testing, and Human-in-the-Loop (HITL) reviews. Use neuro-symbolic AI approaches to shift from probabilistic evaluation to deterministic, rules-based judgin

Week 15: Multi-Agent Systems and Orchestration

Understand the fundamentals of Multi-Agent Systems (MAS) and the unique challenges they present compared to single-agent setups. Compare architectural models, including hierarchical and conversational team structures, and analyze the role of communication, coordination, and strategic interaction within MAS. Learn to implement Agent-to-Agent (A2A) communication protocols for seamless data exchange. Design collaborative multi-agent systems, such as writer-critic setups, to solve complex problems end-to-end, and apply Small Language Models (SLMs) for sub-agents to improve speed and optimize costs.

Week 16: Project Week

Work on a hands-on project building an Enterprise Cybersecurity Threat Detection & Response Agent using multi-agent systems, secure and responsible AI, and industry-relevant cybersecurity tools and frameworks.

Self-Paced Module | NLP Foundations and Transformers

These self-paced modules are designed for you to progress at your own pace, build practical AI skills, explore key concepts, and gain hands-on experience across a wide range of AI applications.

Live Masterclasses Led by JHU Faculty

Gain firsthand industry perspectives and expert insights through engaging masterclasses

Challenges on Implementing LLMs/LLM Grounding

Learn how to design modular, graph-based LLM systems, explore clinical applications of AI-driven decision support, and understand best practices and common pitfalls when using generative AI in software development.

DeepSeek, Low-Cost LLM, Bias

Learn how DeepSeek’s mixture-of-experts architecture enables cost-efficient, high-performance models, understand how low-cost LLMs achieve strong results, explore sources of bias and how to manage them, and see how model distillation makes advanced AI more efficient and accessible.

Model Context Protocol (MCP)

Learn how the Model Context Protocol (MCP) standardizes interactions between models and tools, understand its architecture and workflow, and explore how it enables portable, composable, and efficient tool integration across AI systems.

Context, Attention, and Change

Understand the dynamic nature of context and its impact on team behavior, the role of attention in innovation and informal leadership, and how to engage informal leaders using Social Network Analysis for change management. Learn to integrate AI while maintaining human touchpoints and use the “Voice of the Member” approach for continuous performance improvement.

Live Masterclasses Led by Industry Experts

Gain firsthand industry perspectives and expert insights through engaging masterclasses

Multi-Modal AI Applications

Learn what multimodal AI is and where it is applied, how to work with different data types such as text, images, audio, and video, and how models align and combine multiple modalities. Gain hands-on experience in preparing datasets, building simple multimodal applications, designing effective prompts, and developing end-to-end systems, while also understanding key limitations, risks, and ethical considerati

Anthropic

Learn about Anthropic’s role in the AI landscape, the capabilities of Claude models, and the principles of Constitutional AI and its importance for safety and alignment. Gain hands-on experience with effective prompting, generating structured outputs, and using the Claude API for basic tasks and integrations. Explore how to build simple Claude-powered applications, compare Claude with other AI models, and evaluate ethical considerations in deploying AI systems.

Sample Case Studies

Apply your learning through real-world case studies guided by global industry experts. Please note: All case studies and projects outlined are indicative and subject to change.

Address Resolution

Industry: Logistics Description: Covers problem definition, solution approach, and outcomes for an address resolution use case. Learners implement the solution using a synthetic dataset, applying and comparing classification algorithms such as Decision Trees, Random Forests, Neural Networks, and Logistic Regression. The case explores model performance differences and demonstrates deployment using simple web-based tools for stakeholder use. Skills You Will Learn: Machine Learning, Classification, Model Evaluation

Credit Eligibility Analysis

Industry: Finance Description: Focuses on evaluating customer eligibility for credit cards by organizing and analyzing datasets containing customer demographics and financial attributes. Learners apply a foundational Python workflow, progressing from basic data structures to DataFrames, while using conditional logic, loops, and functions to transform data and generate meaningful business insights. Skills You Will Learn: Python, Data Analysis, Data Processing

Customer Support Automation With Generative and Agentic AI

Industry: E-Commerce Description: Explores the automation of customer support using Generative AI to handle responses, summarization, and multilingual interactions, followed by a transition to Agentic AI systems that can reason, plan, and execute tasks such as retrieving order data and processing requests. The case highlights the shift from content generation to autonomous task execution, along with associated challenges in reliability and risk management. Skills You Will Learn: Generative AI, Agentic AI, Automation

AI-Assisted Production Code Development

Industry: Information Technology Description: Explores how AI-assisted coding accelerates end-to-end development by translating natural language into functional Python code for rapid prototyping, debugging, and optimization. The case highlights the shift toward structured workflows that combine prompt refinement, validation, and engineering best practices to ensure reliable, production-ready systems. Skills You Will Learn: Generative AI, Python, Code Generation

AI-Assisted End-to-End Machine Learning Workflows

Industry: Information Technology Description: Explores how AI-assisted tools accelerate machine learning development by translating high-level ideas into end-to-end pipelines covering data preprocessing, feature engineering, model training, and evaluation. The case highlights early challenges around reproducibility and pipeline quality, followed by a shift toward structured workflows with validation, versioning, and best practices such as experiment tracking and modular design. Skills You Will Learn: Machine Learning, MLOps, Generative AI

Customer Reviews Analysis Using Prompt Engineering

Industry: E-Commerce Description: Explores how large language models are used to analyze customer reviews for sentiment analysis, feedback summarization, issue detection, and feature extraction through prompt engineering instead of traditional model training. The case highlights early gains in rapid deployment, followed by challenges in consistency and robustness at scale, addressed through structured prompt templates, few-shot examples, and evaluation frameworks with guardrails for reliable outputs. Skills You Will Learn: Prompt Engineering, Generative AI, NLP

Clinical-Grade Generative AI Evaluation for Medical Insights

Industry: Healthcare Description: Explores the deployment of Generative AI across clinical workflows such as patient chatbots, clinical note summarization, and medical content generation, highlighting the limitations of traditional software testing in high-stakes healthcare environments. The case introduces a structured, clinical-grade evaluation framework combining automated metrics, clinician-in-the-loop validation, benchmark datasets, and scenario-based testing to detect hallucinations, unsafe outputs, and edge-case failures, ensuring alignment with medical guidelines and regulatory standards. Skills You Will Learn: Generative AI, Model Evaluation, Healthcare AI

Intelligent Knowledge Assistant Using Advanced RAG

Industry: Financial Services Description: Explores the use of Retrieval Augmented Generation (RAG) to enhance large language models with domain-specific, up-to-date information for enterprise use cases such as customer support and internal knowledge search. The case highlights improvements in accuracy through grounded responses and addresses challenges in retrieval quality and latency using advanced techniques such as hybrid search, re-ranking, query rewriting, multi-hop retrieval, and context compression for more precise and context-aware outputs. Skills You Will Learn: Retrieval Augmented Generation, Information Retrieval, Generative

Specialized Clinical AI Using Fine-Tuned Small Models

Industry: Healthcare Description: Explores the use of fine-tuned small language models to deliver cost-efficient, domain-specific AI solutions for healthcare workflows such as customer support, document classification, and internal knowledge retrieval. The case highlights performance improvements through training on curated datasets, alongside challenges in data quality, overfitting, and maintenance, addressed through structured pipelines, evaluation benchmarks, and continuous model updates for reliable deployment. Skills You Will Learn: Model Fine-Tuning, Machine Learning, Healthcare AI

Responsible AI for Automated Customer Support

Industry: E-Commerce Description: Explores the transition from manual customer support to an AI-powered chatbot system designed to reduce response time and operational costs. The case implements a Responsible AI framework focused on transparency, fairness, and data security, incorporating techniques such as reasoning-based prompting, input/output sanitization, and PII masking. Moderation systems are used to ensure safe, scoped, and compliant responses while enabling scalable 24/7 customer support automation. Skills You Will Learn: Responsible AI, Generative AI, AI Safety

AI Legal Research and Analysis Agent

Industry: Legal Technology Description: Explores an AI-powered legal research agent that automates case analysis by combining historical case retrieval, real-time legal updates, and argument evaluation. The system uses Retrieval Augmented Generation (RAG) for case law, web search integration for current precedents, and structured reasoning to identify gaps in legal arguments. Performance is evaluated using DeepEval across tool correctness, task completion, and answer relevance to ensure reliable legal decision support. Skills You Will Learn: Agentic AI, Retrieval Augmented Generation, Model Evaluation

Multi-Agent AI Research and Evaluation System

Industry: Artificial Intelligence / Research Description: Explores a LangGraph-powered multi-agent system that retrieves research papers from arXiv, enriches findings with web data, and synthesizes insights on emerging AI trends. The system is evaluated using a multi-layer framework covering reasoning quality, tool selection accuracy, and execution efficiency. It combines LLM-as-a-Judge approaches with neuro-symbolic evaluation methods to ensure reliable, structured, and validated research outputs. Skills You Will Learn: Multi-Agent Systems, Model Evaluation, Generative AI

Work on hands-on projects and case studies

Engage in hands-on projects and real-world case studies using emerging tools and technologies

  • 3

    hands-on projects

  • 12+

    real-world case studies

  • New Masterclass

    On the Anthropic Series

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FINANCE

Personal Finance Coach and Spending Analyzer

Description

Build a GenAI-powered app that analyzes bank statements, auto-categorizes transactions, and visualizes spending patterns. The system detects anomalies and acts as a conversational financial coach, offering personalized insights and budgeting guidance.

Skills you will learn

  • GenAI
  • NLP
  • Data Analysis
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HEALTHCARE

Clinical Decision Support System

Description

Develop a RAG-based clinical assistant that processes medical knowledge bases to deliver accurate, cited responses to clinical queries. The system improves decision-making by integrating protocols, patient data, and medical literature.

Skills you will learn

  • RAG
  • Prompt Engineering
  • Model Evaluation
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CYBERSECURITY

Threat Detection and Response Agent

Description

Create a multi-agent system that monitors security logs, detects threats, and automates investigation workflows. The solution prioritizes alerts, generates response playbooks, and escalates incidents with detailed evidence.

Skills you will learn

  • Agentic AI
  • Security Analytics
  • System Design

Master in-demand AI tools

Gain hands-on experience with top AI tools

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    Python

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    ChatGPT

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    LangChain

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    LangGraph

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    VS Code (Visual Studio Code)

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    Google Colab

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    Hugging Face

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    Claude Code

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    DeepEval

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    MCP

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    A2A

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    CrewAI

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    RAG (Retrieval Augmented Generation)

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    Reasoning + Acting

Earn a certificate of completion from Johns Hopkins University

Get a globally recognized credential from a top U.S. university and showcase it to your network

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* Image for illustration only. Certificate subject to change.

Meet your faculty

Gain access to world-class coaching from renowned faculty and industry experts.

  • Dr. Ian McCulloh - Faculty Director

    Dr. Ian McCulloh

    Director of AI Executive & Professional Education, Johns Hopkins University

    Served as Chief Data Scientist and MD, AI, Accenture Federal Services

    Author of three books and over 100 peer-reviewed papers

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  • Dr. Pedro Rodriguez - Faculty Director

    Dr. Pedro Rodriguez

    Faculty, Johns Hopkins University AI Program

    Oversees 250+ AI/ML researchers on projects for the Department of Defense, Intelligence Community, and other government agencies

    Brings 20+ years of expertise in AI/ML algorithms for detection, tracking, classification, and sensor fusion

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  • Dr. Iain Cruickshank - Faculty Director

    Dr. Iain Cruickshank

    Faculty Member, Johns Hopkins University

    ML expert applying AI to intelligence, cybersecurity, and social data

    Ph.D, Societal Computing, Carnegie Mellon University School of Computer Science

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  • Dr. William Gray-Roncal - Faculty Director

    Dr. William Gray-Roncal

    Principal Research Scientist - Johns Hopkins University Applied Physics Laboratory

    Expert in data science, neuroscience, AI, and precision medicine.

    Leads cutting-edge research in brain network mapping and analysis.

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  • Dr. Shelby Wilson - Faculty Director

    Dr. Shelby Wilson

    Senior Data Scientist - The Johns Hopkins University Applied Physics Laboratory

    Expert in applied mathematics, computational epidemiology, and ML.

    Over a decade of experience solving real-world problems with mathematical and AI tools.

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  • Dr. Pavankumar Gurazada - Faculty Director

    Dr. Pavankumar Gurazada

    Senior Faculty, Academics, Great Learning

    15+ years of experience in marketing, digital marketing, and machine learning.

    Ph.D. from IIM Lucknow; MBA from IIM Bangalore; IIT Bombay graduate.

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Interact with our mentors

Interact with dedicated and experienced AI experts who will guide you through your learning journey

  • Tanya Glozman - Mentor

    Tanya Glozman linkin icon

    Applied Science - AI/ML, Apple
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  • Bridget Huang-Gregor - Mentor

    Bridget Huang-Gregor linkin icon

    GenAI/ML Engineer at Amazon Web Services (AWS)
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  • Bhaskarjit Sarmah - Mentor

    Bhaskarjit Sarmah linkin icon

    Head of AI Research, Domyn
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  • Jeremy Samuelson - Mentor

    Jeremy Samuelson

    Principal Data Scientist & ML Engineer, Equifax
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Get dedicated career support

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    Resume Review

    Optimize your resume to highlight your best skills and experience, ensuring it stands out to recruiters.

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    LinkedIn Profile Review

    Enhance your LinkedIn profile to increase visibility and showcase your expertise.

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    Interview Preparation

    Gain insider perspectives to understand what recruiters look for and learn strategies to excel in interviews.

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    E-Portfolio Development

    Create an e-portfolio to showcase your projects and skills. Share easily to stand out to recruiters.

Course Fees

The program fee is USD 3,450

Advance your career

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    Understand GenAI foundations and apply techniques to create text, image, and multimedia content

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    Develop and train GenAI models with ML frameworks, and analyze GenAI’s impact on industries and society

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    Learn to build intelligent AI agents to power real-world, agentic workflows and personalized automation

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    Implement best practices and evaluate ethical considerations to mitigate potential risks in GenAI solutions

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Easy payment plans

Avail our EMI options & get financial assistance

  • INSTALLMENT PLANS

    Upto 12 months Installment plans

    Explore our flexible payment plans

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  • discount available

    Scholarship: USD 3,450 USD 3,250

    One Time Discount: USD 3,450 USD 3,275

Third Party Credit Facilitators

Check out different payment options with third party credit facility providers

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*Subject to third party credit facility provider approval based on applicable regions & eligibility

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Application Closes: 23rd Apr 2026

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Application Closes: 23rd Apr 2026

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Application process

Our application process close once the requisite number of participants enroll for the upcoming batch

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    1. Fill application form

    Apply by completing the online application form.

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    2. Application screening

    A panel from Great Learning will review your application to determine your fit for the program.

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    3. Join the Program

    After a final review, you will receive an offer for a seat in the upcoming cohort of the program.

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Batch start date

  • Online · 25th Apr 2026

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Applied Generative AI and Agentic AI

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Program Details

What is the Applied Generative AI Program offered by Johns Hopkins University?

The Certificate Program in Applied Generative AI is a 16-week online program offered by Johns Hopkins University. It is designed to equip professionals with advanced knowledge and practical skills in Generative AI. This Applied Generative AI Program combines theoretical foundations with real-world case studies on cutting-edge topics such as Large Language Models (LLMs), Natural Language Processing (NLP), and Retrieval-Augmented Generation (RAG).

What are the highlights of the Generative AI course?

The Certificate Program in Applied Generative AI from Johns Hopkins University is a comprehensive online learning experience designed to equip professionals with advanced skills in Generative AI. Here are some of the highlights of the program: 


  • Flexible Online Format: The program is delivered online through recorded video lectures, live mentorship sessions, and live monthly faculty-led masterclasses. 

  • World-Class Faculty: The program is taught by world-class faculty and industry experts who have real-world experience leading AI practices at Fortune 500 companies. 

  • Research-Driven Curriculum: The curriculum is designed based on the latest research and developments in Generative AI, ensuring that learners benefit from the most up-to-date and relevant information. 

  • In-Demand Tools and Libraries: Develop proficiency with in-demand tools and frameworks such as Python, Google Colab, BERT, VS Code, vector databases (Chroma, Pinecone), Transformers, Retrieval-Augmented Generation (RAG), and quick fine-tuning methods. 

  • Certificate from JHU: Earn a certificate from Johns Hopkins University upon completion of the program.  

  • Continuing Education Units: Earn 10 Continuing Education Units (CEUs) upon program completion. 

  • Learning support: Get personalized assistance from a dedicated program manager and academic support through the GL community, project discussion forums, and peer groups.

What are the key outcomes of this Gen AI course?

By the end of the Certificate Program in Applied Generative AI, you will be able to:

  • Understand the theoretical foundations of Generative AI and its applications 
  • Apply Generative AI techniques to create text, image, and multimedia content 
  • Implement best practices to mitigate potential risks in Generative AI solutions 
  • Develop and train Generative models using contemporary Machine Learning frameworks. 
  • Evaluate the ethical implications of Generative AI 
  • Critically analyze the impact of Generative AI on various industries and society as a whole.

What is the duration of this Generative AI course with a certificate?

The duration of the Certificate Program in Applied Generative AI is 16 weeks.

What is the structure and format of this Generative Artificial Intelligence program?

The Applied Generative AI program is offered in a flexible online format that includes

  • Recorded video lectures 
  • Interactive mentoring sessions, and 
  • 2 live masterclasses by JHU faculty

What topics are covered in the curriculum of this Generative AI course by JHU?

The topics in the curriculum of this Applied Generative AI program include: 


  • Generative AI Landscape 
  • Python Programming with Generative AI 
  • Foundation of AI 
  • Natural Language Processing And Image Classification 
  • Transformers for Large Language Models 
  • Prompt Engineering 
  • Classification, Content Generation, and Summarization with Gen AI 
  • Secure and Responsible Gen AI Solutions 
  • Developing Agents with LangChain 
  • Retrieval Augmented Generation (RAG) Search 
  • Advanced RAG 
  • Fine-Tuning and Customization of Generative AI

Is there any project included?

Yes, the program includes a project focused on applying Generative AI models to real-world business scenarios. You will begin the project in Week 9 of the program, followed by a one-week break.

Will I receive any learning and Academic support in JHU’s Generative AI course?

Yes, the Certificate Program in Applied Generative AI offers comprehensive learning support, including: 


  • Personalized assistance from a dedicated program manager to stay on track and manage your learning journey effectively. 
  • Academic support through the GL community, project discussion forum, and peer groups.

What is the expected weekly time commitment for this program?

The weekly commitment will be 8-10 hours per week.

Who will be teaching this course?

This course is taught by renowned academicians and leading AI experts with real-world experience in leading AI practices at Fortune 500 companies. Here are some of the faculty and industry mentors teaching this course: 


Dr. Ian McCulloh 

Faculty Leader in AI and Strategy, Johns Hopkins University 

Dr. Pedro Rodriguez 

Faculty Member, Johns Hopkins University 

Dr. Iain Cruickshank 

Faculty Member, Johns Hopkins University 

Dr. Pavankumar Gurazada Senior 

Faculty, Academics, Great Learning 


Note: This is an indicative list and is subject to change based on the availability of faculty and mentors

Will I receive a Gen AI certificate from Johns Hopkins University upon completing this program?

You will receive a Certificate of Completion from Johns Hopkins University upon successful completion of the program. This is a certificate and not a certification.

What are the rankings of JHU?

JHU is a world-renowned university, which is ranked #6 

National University Rankings (U.S. News & World Report, 2025) 

#13 Best Global University (U.S. News & World Report, 2024)

What is the role of Great Learning in this program?

The Applied Generative AI program at Johns Hopkins University is delivered in collaboration with Great Learning. Great Learning is a professional learning company with a global footprint in 14+ countries. Its mission is to make professionals around the globe proficient and future-ready. This program leverages JHU's leadership in innovation, science, engineering, and technical disciplines developed over years of research, teaching, and practice. Great Learning manages the enrollments and provides industry experts, student counselors, course support, and guidance to ensure students get hands-on training and live personalized mentorship on the application of concepts taught by the JHU faculty.

How does this course help in transitioning to GenAI roles?

This Applied Generative AI Program offers a practical, hands-on approach to mastering Generative AI. You will 


  • Learn from JHU faculty and industry leaders. 
  • Build real-world GenAI applications and workflows. 
  • Work on projects and case studies aligned with business needs. 
  • Gain expertise in tools like LLMs, RAG, and Python. 
  • Earn a certificate and 10 CEUs from Johns Hopkins University. 


Together, these elements equip you with the skills, portfolio, and credibility needed to confidently transition into GenAI roles across industries.

Admissions & Eligibility

Who is this program for?

This Applied Generative AI Program is designed for individuals who are looking to explore how Generative AI can address real-world business problems, and is ideal for:

  • Technology professionals 
  • Data professionals 
  • Technology Consultants and Technical Managers 
  • STEM (Science, Technology, Engineering, and Mathematics) Graduates

Can I pursue this program while working full-time?

Yes. The program is designed for working professionals. Classes are scheduled on weekends, allowing you to balance your coursework with full-time professional commitments.

What is the admission process for the Generative AI program?

The admissions for the program close once the required number of participants enroll. Apply early to secure your spot. Here are the steps for admissions:


  • Apply by completing the online application form 
  • A panel from Great Learning will review your application to determine your fit for the program 
  • After a final review, you will receive an offer for a seat in the upcoming cohort of the program
Fee & Payment

What is the total fee for the program?

The total fee for the Applied Generative AI Program is USD 2950.

Are payment plans or financing options available?

Yes, flexible EMI options and financial assistance may be available to eligible candidates. Please check with the admissions team during your application process.

Does the fee include the Generative AI certificate and all learning materials?

The program fee includes access to all course materials, live masterclasses, mentorship, and a certificate of completion from Johns Hopkins University.

Others

What are CEUs, and how are they useful?

Upon successful completion of the program, you will earn 10 Continuing Education Units (CEUs) along with a certificate of completion from Johns Hopkins University. CEUs are a measure of instructional time and reflect the estimated time an average student will spend completing the mandatory components of a course. CEUs are awarded upon successfully completing all course requirements, which may include assessments, assignments, evaluations, or a final project. Earning CEUs allows professionals to demonstrate their commitment to continuous learning and skill advancement

How is Generative AI being used across industries?

Generative AI has become essential in almost every sector to automate processes, enhance customer experiences, streamline operations, and drive innovation. Here is how it is used in various sectors: 


  • Healthcare: Generating medical summaries, patient communication, and drug discovery insights. 
  • Finance: Automating reporting, fraud detection, and personalized financial advice. 
  • Marketing: Creating ad copy, product descriptions, and targeted campaigns. 
  • Technology: Powering AI copilots, code generation, and virtual assistants. 
  • Retail & E-commerce: Enabling personalized recommendations and chatbot support. 
  • Media & Entertainment: Generating scripts, music, visual assets, and game narratives.