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Breaking up is hard to do: Chunking in RAG applications

As you go deeper down the rabbit hole building LLM-based applications, you may find that you need to root your LLM responses in your source data. Fine-tuning an LLM with your custom data may get you a generative AI model that understands your particular domain, but it may still be subject to inaccuracies and hallucinations. This has led a lot of organizations to look into retrieval-augmented generation (RAG) to ground LLM responses in specific data and back them up with sources.With RAG, you cre...

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