{"id":700392,"date":"2025-08-21T09:13:16","date_gmt":"2025-08-21T07:13:16","guid":{"rendered":"https:\/\/www.devoteam.com\/expert-view\/agentic-chunking-makes-your-rag-smarter\/"},"modified":"2025-08-21T09:55:17","modified_gmt":"2025-08-21T07:55:17","slug":"agentic-chunking-makes-your-rag-smarter","status":"publish","type":"expert-view","link":"https:\/\/devoteam.info\/en-pt\/expert-view\/agentic-chunking-makes-your-rag-smarter\/","title":{"rendered":"Stop Losing the Plot: How Agentic Chunking Makes Your RAG Smarter"},"content":{"rendered":"\n
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Written as part of our AI Upskilling Program<\/strong><\/h2>\n\n\n\n

This article was created as part of the Global Devoteam AI Upskilling Program<\/a>, where employees share their knowledge to accelerate their learning. The program’s key objective is to provide a foundation in AI for every employee and apply these new skills in our work. Do you want to work with us? Check out our career opportunities.<\/p>\n\n\n\n

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Career Opportunities<\/a><\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n\n

Retrieval-Augmented Generation (RAG)<\/a> promises to make Large Language Models (LLMs) truly useful for your specific needs. The idea is brilliant: connect an LLM to your own up-to-date documents or data, giving it the information it needs to answer questions accurately and relevantly, without needing constant retraining. Think of it as transforming a closed-book exam for your LLM into an open-book one, allowing it to consult specific knowledge before answering.<\/p>\n\n

Sounds great, right? But if you’ve worked with RAG, you might have felt… well, a bit frustrated.<\/strong> Despite the potential, RAG systems often stumble. Answers can be inaccurate, oddly incomplete, miss the context entirely, or worse, confidently make things up (hallucinate<\/a>).<\/p>\n\n

Here\u2019s the often-overlooked culprit: it\u2019s frequently not the LLM\u2019s generation skills that are lacking, but the retrieval<\/em> step failing.<\/strong> The system simply pulls back the wrong snippets of information. And why does retrieval fail? More often than not, it traces back to how the source documents were prepared \u2013 specifically, how they were chunked<\/strong><\/em>.<\/p>\n\n

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What you’ll read in this article<\/h3>