IBM
RAG for Generative AI Applications Specialization

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IBM

RAG for Generative AI Applications Specialization

Build smarter apps with RAG and GenAI Tools. Get hands-on building GenAI-powered apps using RAG, vector databases, & advanced retrieval tools.

IBM Skills Network Team
Wojciech 'Victor' Fulmyk
Hailey Quach

Instructors: IBM Skills Network Team

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Get in-depth knowledge of a subject
Intermediate level

Recommended experience

1 month
at 10 hours a week
Flexible schedule
Learn at your own pace
Get in-depth knowledge of a subject
Intermediate level

Recommended experience

1 month
at 10 hours a week
Flexible schedule
Learn at your own pace

What you'll learn

  • Build job-ready skills to create GenAI applications using Retrieval-Augmented Generation (RAG)

  • Use advanced RAG frameworks like LangChain and LlamaIndex to boost response quality

  • Leverage vector databases like FAISS and Chroma DB to power efficient semantic search and recommendation systems

  • Design complete RAG apps with Gradio, Python, and popular Large Language Models (LLMs) like IBM Granite, Llama and GPT

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Taught in English
Recently updated!

June 2025

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Specialization - 4 course series

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

Category: Prompt Engineering
Category: Generative AI
Category: Flask (Web Framework)
Category: JSON
Category: Natural Language Processing
Category: Full-Stack Web Development
Category: Application Development
Category: Large Language Modeling

What you'll learn

  • Develop a practical understanding of Retrieval-Augmented Generation (RAG)

  • Design user-friendly, interactive interfaces for RAG applications using Gradio

  • Learn about LlamaIndex, its uses in building RAG applications, and how it contrasts with LangChain

  • Build RAG applications using LangChain and LlamaIndex in Python

Skills you'll gain

Category: Prompt Engineering
Category: Application Development
Category: Application Frameworks
Category: Generative AI
Category: Jupyter
Category: Natural Language Processing
Category: Large Language Modeling
Category: User Interface (UI)
Category: Artificial Intelligence

What you'll learn

  • Differentiate between vector databases and traditional databases based on their functionality and use cases

  • Execute fundamental database operations in ChromaDB, including updating, deleting, and managing collections

  • Understand and apply similarity search techniques, both manually and with ChromaDB, and develop recommendation systems using these techniques

  • Develop a thorough and comprehensive understanding of key internal mechanisms within RAG

Skills you'll gain

Category: NoSQL
Category: Database Architecture and Administration
Category: Artificial Intelligence
Category: Databases
Category: Machine Learning Methods
Category: Database Management
Category: Data Storage Technologies
Category: Generative AI
Category: Database Systems

What you'll learn

  • Differentiate between various retrieval patterns and assess their effectiveness in RAG applications 

  • Implement advanced retrievers and FAISS to optimize information retrieval and similarity search 

  • Design a comprehensive RAG application by integrating LangChain, FAISS, and a front-end UI using Gradio

  • Evaluate retrieval strategies and refine AI-driven search capabilities for improved performance

Skills you'll gain

Category: Generative AI
Category: Real Time Data
Category: Text Mining
Category: Applied Machine Learning
Category: Semantic Web
Category: Artificial Intelligence

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Instructors

IBM Skills Network Team
IBM
84 Courses1,345,510 learners

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IBM

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