Large language models (LLMs) gained widespread popularity after the release of ChatGPT–based on GPT-3. This led to many businesses, including small and medium-sized enterprises (SMEs), to try leveraging ChatGPT and other LLMs to enhance their applications. LLMs handle general tasks well but may struggle with tasks that rely on private business data because this data was not used in training and is not available online. Fine-tuning the model on business data can solve this problem and improve performance for specific domains. SMEs that find fine-tuning to be too costly and resource intensive should consider a vector database as a cheaper and more resource-friendly alternative. AI engineers can use Retrieval-Augmented Generation (RAG) through vector databases to ensure SMEs create innovative AI applications while reducing cost and resources.
Why SMEs Will Benefit from Vector Databases
Creating an LLM from scratch is usually a no-go for SMEs …