Build RAG Applications: Get Started

🎓 IBM Generative AI & RAG Engineering Track
Course: Build RAG Applications: Get Started
Status: ✅ Completed
Instructor: IBM AI Engineering Team
Credential: Verified Certificate – 2025

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📘 Overview #

This course provides a hands-on introduction to Retrieval-Augmented Generation (RAG) — an AI framework that combines retrieval and generation to produce context-aware, accurate, and explainable LLM responses.

Across three progressive modules, I learned to:

• Build RAG pipelines using LangChain and LlamaIndex
• Integrate IBM Granite 3.0 large language models
• Design interactive AI applications with Gradio
• Compare different frameworks for retrieval and data ingestion
• Deploy end-to-end RAG solutions powered by Python + watsonx.ai

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🧩 Course Structure #

Module	Title	Framework	Focus	Status
1	What is RAG	LangChain	Fundamentals of retrieval, embeddings, and grounding LLMs in private data	✅ Completed
2	Build Apps with RAG	LangChain + Gradio	Interactive RAG application (QA bot) using IBM Granite & local vector DBs	✅ Completed
3	Build RAG Apps with LlamaIndex	LlamaIndex	Understanding indexing, retrieval, and query engines; comparison to LangChain	✅ Theory Completed / Lab Skipped (Proxycurl deprecated)

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🎯 Learning Objectives #

By completing this course, I am able to:

• Explain how RAG improves LLM reliability, trust, and factual grounding
• Implement document ingestion, chunking, embedding, retrieval, and generation steps
• Design user-friendly interfaces for AI apps using Gradio
• Compare the design philosophies of LangChain (process-centric) and LlamaIndex (data-centric)
• Integrate enterprise-grade models like IBM Granite 3.0 into RAG workflows

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🧠 Key Concepts & Architecture #

User Query
    ↓
Embed Query (Encoder)
    ↓
Retrieve Top-K Similar Chunks (Vector Store)
    ↓
Augment Query with Retrieved Context
    ↓
Generate Grounded Answer (LLM)
    ↓
Return Context-Aware Response to User

Core Components

Stage	Component	Description
Ingestion	Document, SimpleDirectoryReader	Load and parse raw data from text/PDF/CSV/JSON
Chunking	SentenceSplitter, RecursiveCharacterTextSplitter	Split large documents into nodes/chunks
Embedding	VectorStoreIndex, FAISS/Chroma	Convert text into dense vectors for semantic search
Retrieval	Retriever	Find relevant document chunks based on query similarity
Generation	LLM (IBM Granite)	Produce natural-language output grounded in context
Interface	Gradio	Web-based user interface for input/output

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🧩 Module Summaries #

Module 1 – What is RAG #

• Learned how retrieval enhances LLM accuracy.
• Built a simple document summarization RAG pipeline with LangChain.
• Covered embeddings, similarity metrics, and vector databases (FAISS, Chroma).
  📁 Module1_README.md

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Module 2 – Build Apps with RAG #

• Designed an interactive QA application using Gradio + LangChain.
• Integrated IBM Granite 3 for grounded answers over uploaded PDFs.
• Demonstrated full workflow: load → split → embed → retrieve → generate → UI.
  📁 Module2_Build_Apps_with_RAG_README.md

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Module 3 – Build RAG Apps with LlamaIndex #

• Explored LlamaIndex concepts: Document ingestion, VectorStoreIndex, and QueryEngine.
• Compared LangChain vs LlamaIndex (process- vs data-centric design).
• Understood SentenceSplitter, ResponseSynthesizer, and QueryEngine automation.
• Note: The “AI Icebreaker Bot” lab was skipped due to deprecated Proxycurl API; however, the LlamaIndex theory and pipeline logic were fully mastered.

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🚀 Outcomes #

By completing the course, I have:

• Built two end-to-end RAG pipelines (LangChain + Gradio).
• Learned to design, implement, and evaluate retrieval-grounded LLM apps.
• Gained practical knowledge in embedding models, vector databases, and LLM orchestration frameworks.
• Acquired a transferable workflow applicable to future Cloud, IoT, and Agentic AI projects.

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🏗️ Repository Structure #

📁 Build_RAG_Applications_Get_Started/
│
├── image.png                           # Certificate
├── Module1_README.md                   # Module 1 summary
├── Module2_Build_Apps_with_RAG_README.md  # Module 2 summary
└── README.md                           # (this file)

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🧭 Next Steps #

• Continue to “Build RAG Applications: Get Advanced” for hybrid retrieval and evaluation metrics.
• Re-implement a self-coded RAG pipeline using open-source LLMs (Llama 3 / Mistral) instead of watsonx.ai.
• Integrate RAG into the Cloud & IoT portfolio project for intelligent device data retrieval.

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🏁 Key Takeaway #

RAG is not just a technique — it’s the foundation of modern, trustworthy AI systems.
Mastering both LangChain and LlamaIndex empowers developers to build scalable, explainable, and enterprise-ready generative AI applications.