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RAG Pipeline: 7 Iterations Explained!
Cyril Imhof
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🎯 MASTER SERIES - RAG 8: Ingestion and Parsing Text Data Using Document Loaders Welcome to Episode 8 of the RAG (Retrieval-Augmented Generation) Master Series, where we dive deep into Ingestion and Parsing Text Data — a critical step in building intelligent retrieval systems. In this session, you’ll learn how to efficiently load, parse, and preprocess textual data using powerful Document Loaders for your RAG pipelines. Whether you're working with PDFs, text files, web pages, or structured documents — this episode gives you hands-on insight into converting raw data into a searchable, vectorized knowledge base. 🔍 In this video, you’ll learn: ✅ What data ingestion means in RAG pipelines ✅ How Document Loaders work (LangChain / LlamaIndex style) ✅ Handling multiple file formats — TXT, PDF, CSV, and more ✅ Parsing techniques for clean and structured text ✅ Integrating loaders into your preprocessing workflow ✅ Best practices for large-scale ingestion and scalability 🧠 Why This Matters: Data ingestion and parsing form the backbone of Retrieval-Augmented Generation systems. Without proper document loading and cleaning, even the best LLMs fail to deliver accurate, context-rich answers. Learn how to build strong data foundations for your AI projects. 🚀 Next in the Series: Stay tuned for RAG 9 – Chunking Strategies and Embeddings Optimization! 📘 Watch Previous Episodes: 1️⃣ RAG 1 – Introduction to Retrieval-Augmented Generation 2️⃣ RAG 2 – Architecture & Components 3️⃣ RAG 3 – Setting Up Your Knowledge Base ... and more! 💡 Who Should Watch: AI & Data Science Enthusiasts Machine Learning Engineers Full-Stack AI Developers Students & Professionals building LLM-based applications 🔔 Subscribe & Stay Updated: Join the community to master RAG, LLMOps, and AI deployment step-by-step. 📩 Subscribe now: [Your Channel Link] 👍 Like | 💬 Comment | 🔗 Share this video #RAG #LangChain #LLM #DocumentLoaders #AI #DataIngestion #VectorDatabases #MachineLearning #LLMOps #DeepLearning #ArtificialIntelligence
