Prompt Engineering
Prompt engineering is the first step toward talking with Generative AI models (LLMs). Essentially, it’s the process of crafting meaningful instructions to generative AI models so they can produce better results and responses. The prompts can include relevant context, explicit constraints, or specific formatting requirements to obtain the desired results. 
Retrieval Augmented Generation (RAG)
Retrieval Augmented Generation (RAG) is an AI framework for retrieving facts from an external knowledge base to ground large language models (LLMs) on the most accurate, up-to-date information and to give users insight into LLMs' generative process. It improves the quality of LLM-generated responses by grounding the model on external sources of knowledge to supplement the LLM’s internal information. Implementing RAG in an LLM-based question-answering system has two main benefits: it ensures that the model has access to the most current, reliable facts and that users have visibility to the model’s sources, ensuring that its claims can be checked for accuracy and ultimately trusted. In this accelerator, we will: