RAG vs Fine-Tuning: Choosing the Right Strategy for Your LLM Use Case
As enterprises race to deploy AI-powered applications, one question consistently surfaces: Should we use Retrieval-Augmented Generation (RAG) or Fine-Tuning? While both approaches enhance Large Language Models (LLMs), they solve fundamentally different problems. Understanding when to use RAG, when to fine-tune, and when to combine both can save organizations significant development time, infrastructure costs, and operational complexity. This guide explores the strengths, limitations, and real-world use cases of each approach to help decision-makers choose the right strategy for scalable AI solutions.
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