Get ready to power up your resume with the GenAI development skills employers need. During this course you’ll explore core prompt engineering strategies—like in-context learning and chain-of-thought—and create and manage robust prompt templates. Plus, you’ll follow best practices to handle common errors and experiment with different LLMs and configurations to strengthen your outputs.
Develop Generative AI Applications: Get Started
Develop Generative AI Applications: Get Started
This course is part of IBM RAG and Agentic AI Professional Certificate
Instructors: Hailey Quach
Included with 
Gain insight into a topic and learn the fundamentals.
Intermediate level
Recommended experience
8 hours to complete
3 weeks at 2 hours a week
Flexible schedule
Learn at your own pace
What you'll learn
Master the basics of GenAI and the LangChain framework, focusing on how prompt engineering and in-context learning to enhance AI interactions
Apply prompt templates, chains, and agents to create flexible and context-aware AI applications using LangChain's modular approach
Develop a GenAI web application with Flask, integrating advanced features such as JSON output parsing for structured AI responses
Evaluate and compare different language models to select the most suitable for specific use cases, ensuring optimal performance and reliability
Skills you'll gain
Details to know
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Recently updated!
May 2025
Assessments
7 assignments
Taught in English
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This course is part of the IBM RAG and Agentic AI Professional Certificate
When you enroll in this course, you'll also be enrolled in this Professional Certificate.
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There are 3 modules in this course
This module introduces the basics of generative AI and provides an overview of the most common tools, frameworks, and models used in AI development. You'll learn fundamental prompt engineering principles that help maximize AI model capabilities through thoughtful input design. Special emphasis is placed on the LangChain framework—its features, benefits, and role in designing and writing AI applications. The module covers practical techniques for creating reusable prompt templates that improve consistency, implementing in-context learning strategies, and optimizing AI responses through iterative prompt refinement.
What's included
9 videos3 readings3 assignments1 app item1 discussion prompt4 plugins
In this module, you will explore LangChain and its advanced features to build generative AI applications. You’ll learn about each core component of LangChain, how to incorporate them to create complex workflows and introduce generative AI into your applications.
What's included
3 videos1 reading2 assignments1 app item2 plugins
In this module, you’ll build a Flask web app that uses the power of generative AI using LangChain. You’ll learn how to integrate AI into your application, leverage LangChain’s JsonOutputParser to produce structured outputs, and fine-tune prompt engineering to generate actionable JSON responses. Along the way, you’ll compare and evaluate different language models—including Llama 3, Granite, and Mixtral—to discover which best suits your needs.
What's included
4 videos3 readings2 assignments1 app item4 plugins
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Frequently asked questions
Skills in building generative AI applications are in demand for roles like AI developer, machine learning engineer, prompt engineer, and full-stack developer with AI experience.
Not necessarily. If you’re a Python developer, you can start building with generative AI using tools like LangChain, Flask, and Gradio—no advanced ML background required.
Generative AI apps require prompt engineering and LLM integration, but you’ll still use familiar web frameworks and developer tools to build and deploy them.
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Financial aid available,