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Machine Learning Mastery0 Hot0 comments8 min read
RAG vs. Fine-Tuning for Domain Adaptation: When to Use Which
A comparison of retrieval-augmented generation (RAG) and fine-tuning for adapting large language models to a domain, explaining that RAG changes what a model sees at inference time (good for large or frequently changing knowledge and auditability), while fine-tuning changes the model's weights (good for consistent tone, structure, or vocabulary but unreliable for injecting new facts). Two working code examples are given: a TF-IDF-based RAG pipeline over incident runbooks using the Anthropic API, and a LoRA fine-tuning setup for classifying customer complaints into a strict taxonomy. A six-point decision framework helps determine whether to use RAG, fine-tuning, or both, noting that roughly 60% of production LLM deployments reportedly combine both approaches.
Source: https://machinelearningmastery.com/rag-vs-fine-tuning-for-domain-adaptation-when-to-use-which. 8 Sync News only summarizes and links out; content copyright belongs to the authors and original sources.
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