RAG: The Power of Text Splitting for Improving Retrieval: A Developer’s Handbook

Author(s): Md Monsur ali

Originally published on Towards AI.

Explore a Variety of Techniques to Enhance Language Model Efficiency: Character, Semantic, Contextual, and Multimodal Approaches

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When working with large language models (LLMs), one of the most overlooked but vital strategies is text splitting. Whether you’re building a retrieval-augmented generation (RAG) system or simply feeding large datasets into an LLM for processing, how you split your text can dramatically affect performance.

Language models operate within fixed context windows, which limit the amount of text you can feed them at once. On top of that, models perform better when they process concise, relevant chunks of information rather than a disorganized deluge of data. This is where text splitting comes in — a technique for breaking down large text into smaller, optimized pieces that make language models more effective at their task.

In this guide, we’ll explore different text splitting, ranging from basic to advanced techniques, with practical examples using LangChain, Ollama embeddings, and Llama 3.2. By the end, you’ll have a solid understanding of each method, when to use them, and how they can improve your retrieval performance.

Text splitting is a critical technique for optimizing the performance of language model applications. By breaking down large data into smaller, manageable chunks,… Read the full blog for free on Medium.

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Published via Towards AI