Sarveshwaran Rajagopal’s Post

Are you working in RAG and not getting better responses? 🤔 Chunking is one strategy you should be re-evaluating. 💡 As we strive to improve our Retrieval-Augmented Generation (RAG) models, it's essential to revisit fundamental techniques like chunking. 📚 But what exactly is chunking, and how can we leverage its semantic variant to enhance our results? 🤔 ---------------- What is Chunking? 🤔 Chunking is a natural language processing (NLP) technique that involves breaking down text into smaller, manageable units called chunks. 📝 These chunks can be phrases, sentences, or even paragraphs, depending on the task. Effective chunking ensures that context is preserved while making it easier to process large datasets or documents. ---------------- What is Sliding Window Chunking? 💡 Sliding Window Chunking takes the concept of chunking further by overlapping chunks. Instead of clean cuts, each chunk overlaps with a portion of the previous chunk, preserving critical context between segments. This approach ensures: 💥 Context Preservation: Avoids breaking sentences or losing essential information. 💥 Flexibility: Allows you to adjust chunk size and overlap to fit your task. Enhanced Performance: Improves results in tasks like retrieval, summarization, and question answering. ---------------- Three Key Aspects of Sliding Window Chunking 📝 1️⃣ Overlap for Context: Chunks share overlapping regions to maintain semantic flow. 2️⃣ Adaptable Parameters: Customize chunk size and overlap to meet your model's needs. 3️⃣ Scalable for NLP Tasks: Works well with large datasets for tasks like RAG, language modeling, and conversational AI. ---------------- Why and Where Should You Use Sliding Window Chunking? 🤔 1️⃣ Retrieval-Augmented Generation (RAG): Improves document understanding and retrieval accuracy. 2️⃣ Text Summarization: Ensures summaries capture complete ideas without losing context. 3️⃣ Conversational AI: Enhances dialogue generation by maintaining continuity across chunks. Comment below if you'd like to see video explanations on chunking strategies! 📹 Let’s discuss how semantic chunking can elevate your RAG models and improve your NLP tasks! 💬 Fixed Length Chunking: https://lnkd.in/gjNRd6Ni Semantic Chunking: https://lnkd.in/g2PMC3t4 Follow: Sarveshwaran Rajagopal #Chunking #NLP #RAG #InformationRetrieval #TextSummarization #ConversationalAI