Two approaches compete to adapt language models to your data. Discover their respective use cases and technical limits.
To leverage generative AI for specific use cases in your business, you need to connect it to your domain data (product catalogs, brand guidelines, support history). Two major technical approaches compete: **Fine-tuning** (custom training a model) and **RAG** (Retrieval-Augmented Generation). Choosing the wrong method can result in significant wasted spend.
RAG works like a student taking an open-book exam. When a user asks a question, the system retrieves relevant information from your private databases and provides it to the model in its context window.
Fine-tuning involves modifying the neural network weights of a model by training it on thousands of custom question-and-answer examples. It is the equivalent of a student studying intensively before an exam to answer without notes.
If you need to supply precise, dynamic facts (e.g., stock levels or refund policies), use RAG. If you need to teach the model how to behave or speak (e.g., write copy like your top writer), use Fine-tuning.
The most successful AI architectures often combine both: a model fine-tuned to adopt your company's tone, using a RAG system to retrieve reliable facts in real time.
Digital acquisition and media strategy experts.