RAG allows organizations to control the behavior of a pretrained AI model by controlling the types of responses the model can give to various queries. Using RAG, it’s possible to take a generic existing model that was not designed for any particular use cases, then tailor it to behave in particular ways that align with a company’s needs. For instance, a business could use RAG to help ensure that a chatbot based on a generic model responds to questions about the company’s products in certain ways.
Fine-tuning provides another way to customize the behavior of a pretrained model. Fine-tuning typically involves feeding a pretrained model additional data (such as information that helps it lebanon whatsapp number data understand products or processes unique to your business) and/or modifying parameters that control how the model behaves. be fine-tuned varies from one model to the next; open-source models are typically more flexible. But even proprietary models, such as those behind ChatGPT and Gemini, support some fine-tuning functionality.
The important thing to keep in mind, however, is that RAG and fine-tuning are both complex and intensive processes. They require less time and effort than building a model from scratch (and RAG in particular tends to require fewer resources to implement), but they still require deep expertise in how GenAI models work, as well as a well-designed data platform that supplies the right types of data – and data of sufficient quality and quantity – to enable effective model customization.