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LLM Apps in Production (RAG + Vector DB + Caching) · Lesson

Data Loading and Text Chunking Basics

Learn how to load unstructured data and apply effective text chunking strategies for optimal retrieval performance.

Loading Data for RAG

Welcome to Lesson 2! In Retrieval Augmented Generation (RAG), the first step is always to get your data ready. This means loading your information and preparing it for the Large Language Model (LLM).

Most real-world data is unstructured, meaning it doesn't fit neatly into rows and columns like a spreadsheet. Think of documents, web pages, or books.

Common Unstructured Data Sources

RAG systems can work with many types of unstructured data. Here are some common examples:

  • Text files (.txt): Simple, plain text documents.
  • PDFs (.pdf): Often contain text, images, and complex layouts.
  • Word Documents (.docx): Rich text with formatting.
  • Web Pages (.html): Content from websites.
  • Databases/APIs: Text extracted from various fields.

The goal is to extract the raw text content from these sources.

All lessons in this course

  1. Choosing an LLM Provider
  2. Data Loading and Text Chunking Basics
  3. Building a Simple RAG Pipeline
  4. Testing & Evaluating Your RAG App
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