Retrieval-Augmented Generation (RAG)

RAG allows users to index their own data in a vector database, which can then be used by a large language model to access relevant information when answering questions or generating text. It facilitates the use of domain-specific knowledge without requiring the retraining of language models, which reduces the time and resources needed. With RAG, users can increase the quality and utility of their language model outputs and benefit from their data.

It is useful for applications that require up-to-date information or access to wide range of data sources. e.g. Chatbots that answer topical questions, customer service AI that provides current information, or research tools that need to pull in the latest scientific data.

Fine-tuning for Open-source Large Language Models

Fine-tuning enables users to further train large language models with domain-specific datasets to improve their performance for specific tasks, while preserving their general language knowledge. Instead of training the model from scratch, fine-tuning is more cost-effective and can achieve high accuracy especially with limited data.

It is beneficial for applications that need a high degree of specialization to adapt models for specific industries like legal or healthcare, for sentiment analysis in customer feedback, or for classification tasks such as organizing documents into categories.

DALL.E 3

DALL.E 3 is the latest model for text-to-image generation offered by Microsoft Azure OpenAI services. It builds on the capabilities of its predecessors by significantly improving the fidelity and quality of the images it generates from text prompts.