Cornellius Y.'s Linkedin Analytics

9 𝐂𝐡𝐮𝐧𝐤𝐢𝐧𝐠 𝐒𝐭𝐫𝐚𝐭𝐞𝐠𝐲 𝐅𝐨𝐫 𝐑𝐀𝐆 𝐭𝐡𝐚𝐭 𝐲𝐨𝐮 𝐬𝐡𝐨𝐮𝐥𝐝 𝐤𝐧𝐨𝐰. RAG is only as good as the data indexed within their knowledge base. You should know these Chuking Strategies to improve the RAG metrics performance. 𝐅𝐢𝐱𝐞𝐝-𝐒𝐢𝐳𝐞 𝐂𝐡𝐮𝐧𝐤𝐢𝐧𝐠: ↳Splits text into equal parts based on a predetermined word or token count ↳Processes content in a straightforward and uniform manner ↳Great for quick segmentation when simple, predictable divisions suffice • 𝐒𝐞𝐧𝐭𝐞𝐧𝐜𝐞-𝐁𝐚𝐬𝐞𝐝 𝐂𝐡𝐮𝐧𝐤𝐢𝐧𝐠: ↳Divides text using natural sentence boundaries detected by NLP tools ↳Ensures each segment contains complete, coherent sentences ↳Excellent for preserving meaning and context in text segmentation • 𝐒𝐞𝐦𝐚𝐧𝐭𝐢𝐜-𝐁𝐚𝐬𝐞𝐝 𝐂𝐡𝐮𝐧𝐤𝐢𝐧𝐠: ↳Groups sentences together based on their underlying semantic similarity ↳Uses embeddings or transformer models to assess and merge related content ↳Ideal for maintaining coherent thematic segments in complex documents • 𝐑𝐞𝐜𝐮𝐫𝐬𝐢𝐯𝐞 𝐂𝐡𝐮𝐧𝐤𝐢𝐧𝐠: ↳Repeatedly splits text into smaller segments until a specific condition is met ↳Perfect for ensuring all segments comply with strict size requirements in varied documents • 𝐒𝐥𝐢𝐝𝐢𝐧𝐠-𝐖𝐢𝐧𝐝𝐨𝐰 𝐂𝐡𝐮𝐧𝐤𝐢𝐧𝐠: ↳Creates overlapping segments by moving a fixed-size window over the text ↳Repeats content across chunks to ensure continuity of context ↳Excellent for capturing transitional context across adjacent text segments • 𝐇𝐢𝐞𝐫𝐚𝐫𝐜𝐡𝐢𝐜𝐚𝐥 𝐂𝐡𝐮𝐧𝐤𝐢𝐧𝐠: ↳Leverages document structure by splitting content at headings, sections, or subheadings ↳Organizes text into nested segments that reflect its natural hierarchy ↳Ideal for preserving multi-level context in structured documents • 𝐓𝐨𝐩𝐢𝐜-𝐁𝐚𝐬𝐞𝐝 𝐂𝐡𝐮𝐧𝐤𝐢𝐧𝐠: ↳Segments text by identifying and grouping content related to specific topics ↳Uses topic modeling or clustering to determine thematic boundaries ↳Great for focusing retrieval on distinct subject areas within multi-topic documents • 𝐌𝐨𝐝𝐚𝐥𝐢𝐭𝐲-𝐒𝐩𝐞𝐜𝐢𝐟𝐢𝐜 𝐂𝐡𝐮𝐧𝐤𝐢𝐧𝐠: ↳Identifies and separates different content types (e.g., text, images, tables) ↳Applies dedicated chunking methods tailored to each modality ↳Perfect for mixed-media documents requiring specialized handling • 𝐀𝐠𝐞𝐧𝐭𝐢𝐜 𝐂𝐡𝐮𝐧𝐤𝐢𝐧𝐠: ↳Uses an intelligent agent to decide where natural breakpoints should occur dynamically ↳Adapts segmentation based on content context and task-specific requirements ↳Ideal for optimizing chunk boundaries to enhance performance in downstream tasks I will write further about the chunking strategies for RAG, so visit my newsletter so you don't miss it! 📩Newsletter Non-Brand Data: https://lnkd.in/g639tmpD 🔗RAG-To-Know Repository: https://lnkd.in/gzjS23Sv