Text splitting is a vital strategy for enhancing the performance of large language models (LLMs). This technique breaks down large text into smaller, optimized pieces, making LLMs more effective. The guide explores various text splitting methods, from basic to advanced, with practical examples involving LangChain, Ollama embeddings, and Llama 3.2. These techniques help in building efficient retrieval-augmented generation (RAG) systems and improve overall retrieval performance.
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