By using training models, enterprises can obtain a myriad of benefits, including enhanced performance, resource efficiency, and continuous improvement. While technologies such as Retrieval Augmented Generation (RAG) already exist for incorporating local data with generative AI models, training models offer distinct advantages that RAG-based models cannot replicate:
- Enhanced performance: When users can upload their own training data, AI models can be trained in more specific tasks. The nature of fine-tuning enables AI-generated answers to be far more accurate than general models. Also, businesses can upload private and industry-specific data for AI models so that the AI system can become an expert on both the specific business and the industry. This enables AI to perform both higher-value and higher-difficulty tasks for enterprises, which would vastly increase productivity.
- Resource efficiency: Training models support greater resource efficiency in terms of computing power. Because the model is being trained on specific data, models with fewer pre-loaded parameters can be used in lieu of more data-intensive models. By running smaller, more specialized models, enterprises can save significant finances on computing and power costs over the long term because the trained model can perform to expectations with fewer parameters. Also, training models can be easily deployed using containers and they require less database and data pipeline setup when compared to RAG-based solutions.
- Continuous improvement: As more data is collected, training models can be continuously updated with the latest information. Initially, AI models could only use pre-captured data, to the exclusion of current events and new information. By using fine-tuned models, enterprises can ensure that their AI systems are always trained with the most up-to-date information, guaranteeing that the content they generate is accurate and current.