When building enterprise AI applications, two main approaches dominate: Retrieval-Augmented Generation (RAG) and Fine-Tuning. Understanding the distinction is vital for optimizing compute costs and ensuring accurate outputs.
RAG allows the model to search through your proprietary knowledge bases dynamically, solving hallucination issues effectively. Fine-tuning builds intuition into the model but is expensive to update. For 90% of startups, robust RAG pipelines offer superior agility and ROI.
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