Every company developing AI faces the same important decision point. How do you connect your huge language model to current corporate data or retrain the model using your examples? This is the RAG vs. fine-tuning choice, and getting it wrong may cost months of engineering work and a significant portion of your AI budget. Simply said, Retrieval-Augmented Generation (RAG) is a mechanism for obtaining information from an external knowledge base. When you ask a question, it inputs the information into the model. Fine-tuning works the opposite way. It retrains the model's internal weights, storing the information inside the model itself. Both are appropriate approaches to developing a domain-specific LLM. However, they are addressing distinct issues. They also have entirely separate cost and maintenance characteristics.
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