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     <p><b>Data</b>. To create this example, we utilized three datasets containing questions and answers on Vertex AI, HR, and Datacenter. You can find them on Hugging face for reference (<a href="https://huggingface.co/datasets/fredmo/dc_rules_dataset" target="_blank">huggingface.co/fredmo</a>).</p>
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     <p><b>Chunking</b> is necessary to make RAG work effectively by improving information precision, retrieval efficiency, and addressing input limitations. The ideal size and overlap depend on the specific task, data, and query patterns and often need to be determined by experimentation and iterative optimization. Balancing chunk size and context is key to achieving optimal RAG performance. The AI receives a broader range of information from the text leading to more accurate and insightful results.</p>