Sarveshwaran Rajagopal’s Post

Are you using RAG but not getting optimal responses? 🤔 One critical aspect to revisit is chunking. 💡 Chunking is an NLP technique that breaks text into manageable pieces like sentences or paragraphs. But not all chunking methods are created equal. Techniques like Sliding Window Chunking and Hierarchical Chunking can significantly improve context retention and response quality. 📌 Sliding Window Chunking ensures overlapping chunks to preserve context and avoid breaking critical information. 📌 Hierarchical Chunking organizes chunks into layers (e.g., sentences → paragraphs → documents) for deeper semantic understanding. ---------------- Where to use these? 🎯 RAG Models: Better retrieval and generation synergy. 🎯 Summarization: Granular yet cohesive summaries. 🎯 Conversational AI: Context-aware interactions. Want detailed explanations or tutorials? Comment below! 📹 ---------------- Fixed Length Chunking: https://lnkd.in/gjNRd6Ni Semantic Chunking: https://lnkd.in/g2PMC3t4 ---------------- Follow: Sarveshwaran Rajagopal #HierarchicalChunking #NLP #RAG #InformationRetrieval #TextSummarization #ConversationalAI