This article discusses various AI reference architectures, including Prompt Engineering, Retrieval Augmented Generation (RAG), Fine-Tuning, and Pretraining. Prompt Engineering involves designing input prompts to guide the behavior of a Large Language Model (LLM) and is characterized by its ease of use, minimal infrastructure requirements, and ability to develop and experiment at low cost. RAG enhances performance by integrating external knowledge through a retrieval system that fetches relevant data from an external storage layer, allowing for dynamic updates, domain-specific contextualization, and cost efficiency. Fine-Tuning involves using an existing pre-trained LLM and adapting it based on a small dataset for a specific task, offering customization, cost efficiency, and output control. Pretraining a model involves training it on a large volume of data without using any prior weights from an existing model, allowing for avoiding biases, unique data, and different languages. The article also introduces Dagster as a tool that can be used to implement these architectures, providing an example of how to use it for fine-tuning a model.