In this course, you’ll access Mistral AI’s collection of open source and commercial models, including the Mixtral 8x7B model, and the latest Mixtral 8x22B. You’ll learn about selecting the right model for your use case, and get hands-on with features like effective prompting techniques, function calling, JSON mode, and Retrieval Augmented Generation (RAG).

In detail: 1. Access and prompt Mistral models via API calls for tasks, and decide whether your task is either a simple task (classification), medium (email writing), or advanced (coding) level of complexity, and consider speed requirements to choose an appropriate model. 2. Learn to use Mistral’s native function calling, in which you give an LLM tools it can call as needed to perform tasks that are better performed by traditional code, such as querying a database for numerical data. 3. Build a basic RAG system from scratch with similarity search, properly chunk data, create embeddings, and implement this tool as a function in your chat system. 4. Build a chat interface to interact with the Mistral models and ask questions about a document that you upload. By the end of this course, you’ll be equipped to leverage Mistral AI’s leading open source and commercial models.