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Generative AI Application Development Fundamentals

PremiumIntermediateCourse

Learn GenAI development with hands-on training in prompt engineering, LangChain workflows, and model optimization to build reliable AI applications for real-world use.

Language

  • English

Topic

  • Artificial Intelligence

Industries

  • Information Technology

Skills You Will Learn

  • Flask, Generative AI, Gradio, LangChain, LLMs, Python

Offered By

  • IBMSkillsNetwork

Estimated Effort

  • 8 Hours

Platform

  • SkillsNetwork

Last Update

  • February 18, 2026
About this Course
Get ready to elevate your technical portfolio with real, job-ready GenAI development skills. In this course, you’ll break down the fundamentals of prompt engineering, explore in-context learning strategies, and design reusable prompt templates that increase consistency and accuracy across AI outputs. 

After completing this course, you will be able to:
  • Explain core GenAI concepts, including prompt engineering fundamentals and in-context learning.
  • Differentiate between prompt templates, chains, and agents within the LangChain framework.
  • Apply prompt engineering strategies to design structured, reusable prompts for consistent model behavior.
  • Develop a functional generative AI application using Python, Flask, and LangChain components.
  • Analyze model performance across multiple LLMs to determine their strengths and suitability for specific tasks.
  • Construct a complete GenAI workflow that integrates user input, backend reasoning, and structured outputs using JSON parsing. 

You’ll learn practical techniques for refining prompts, troubleshooting unpredictable model behavior, and experimenting with different LLM configurations to strengthen the quality of your responses.

From there, you’ll dive into LangChain’s modular ecosystem, mastering chains, tools, and agents to build context-aware applications that can reason, plan, and respond more effectively. Hands-on labs guide you through building a fully functional generative AI application in Python that accepts user input, applies your backend logic, and delivers structured results. You’ll also explore Flask and Gradio to create interactive web interfaces that showcase your GenAI system in action.
 
By the end of the course, you’ll have proven experience designing, optimizing, and deploying end-to-end GenAI applications using industry-recognized tools and workflows—skills that employers now expect from modern AI developers. Enroll and start building with confidence.

Course Prerequisites


The following skills are required to be successful with this course: 
  • Working knowledge of Python
  • Basic understanding of AI and web development

Course Syllabus


Welcome to the Course
  • Course Introduction
  • RAG and Agentic AI Professional Certificate Overview
  • Course Overview
  • Reading: Helpful Tips for Course Completion

Module 1: Foundations of Generative AI and Prompt Engineering
  • Module Summary and Learning Objectives
  • [Optional] Generative AI Essentials
    • About this Optional Lesson
    • Introduction to Generative AI
    • What Are Generative AI Models?
    • What Is NLP (Natural Language Processing)?
    • Reading: Comprehensive Guide to Generative AI
    • Practice Quiz: Generative AI Essentials
  • Working with Prompt Engineering and Prompt Templates
    • Introduction to In-Context Learning
    • Introduction to LangChain
    • Advanced Methods of Prompt Engineering
    • LangChain LCEL Chaining Method
    • (Optional) Reading: What Is Prompt Engineering, and Why Do…
    • Lab: Master Prompt Engineering and LangChain
    • Practice Quiz: Working with Prompt Engineering and Prompt Templates
  • Module Summary and Evaluation
    • Summary and Highlights: Foundations of Generative AI and Prompt Engineering
    • Cheat Sheet: Foundations of Generative AI and LangChain
    • Graded Quiz: Foundations of Generative AI and Prompt Engineering
    • [Optional] Discussion Prompt: Meet and Greet

Module 2: Introduction to LangChain in GenAI Applications
  • Module Summary and Learning Objectives
  • LangChain Core Components and Advanced Features
    • Reading: Recap: Introduction to LangChain
    • LangChain: Core Concepts
    • LangChain Chains and Agents for Building Applications
    • (Optional) LangChain LCEL Chaining Method
    • Lab: Build Smarter AI Apps: Empower LLMs with LangChain
    • Practice Quiz: LangChain Core Components and Advanced Features
  • Module Summary and Evaluation
    • Summary and Highlights: Introduction to LangChain in GenAI Applications
    • Cheat Sheet: Introduction to LangChain in GenAI Applications
    • Graded Quiz: Introduction to LangChain in GenAI Applications

Module 3: Build a Generative AI Application with LangChain
  • Module Summary and Learning Objectives
  • Application Development Workflow with Generative AI
    • Choose the Right AI Model for Your Use Case
    • From Idea to AI: Building Applications with Generative AI
    • Introduction to Flask
    • (Optional) Reading: Flask: A Gateway to Web Development in…
    • (Optional) Reading: Python with Flask for Large Scale Projects
    • Cheat Sheet: Web Development Using Flask
    • Hands-on with GenAI: Choosing the Right Model for Your…
    • Practice Quiz: Application Development Workflow with Generative AI
  • Module Summary and Evaluation
    • Summary and Highlights: Build a Generative AI Application with LangChain
    • Cheat Sheet: Build GenAI Application with LangChain
    • Graded Quiz: Build a Generative AI Application with LangChain

Course Wrap Up
  • Course Wrap-up
  • Congratulations and Next Steps
  • Team and Acknowledgments