Getting Started with Mistral from Coursera

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.

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Taught by highly respected AI experts in the field

Develops skills in retrieval augmented generation

Relevant for javascript developers

Emphasizes effectiveness of using Mistral models

Reviews summary

Practical guide to mistral ai models

According to students, this course provides a highly practical and up-to-date introduction to Mistral AI's models, including Mixtral 8x7B and 8x22B. Learners found the hands-on labs and coding exercises particularly beneficial for implementing features like function calling and building a basic Retrieval Augmented Generation (RAG) system. The API interaction and model selection guidance are highlighted as immediately applicable skills. While most reviews are overwhelmingly positive, a minority of learners mentioned that the pace might be challenging for those entirely new to LLM APIs or that some sections could benefit from more advanced troubleshooting and real-world deployment tips.

Covers the latest Mistral AI models and features.

"I found it excellent for getting up to speed with Mistral."

"I learned the essentials of interacting with Mistral APIs."

"I felt this course delivered exactly what it promised: a hands-on guide to using Mistral's models."

Strong emphasis on practical API interactions and usage.

"I learned how to access and prompt Mistral models via API calls for tasks."

"The JSON mode and API interaction sections are immediately applicable to my work."

"I found it covers the essentials of interacting with Mistral APIs."

Simplifies complex topics like RAG and function calling.

"The explanations of function calling and RAG were particularly clear and actionable."

"Clear, concise, and straight to the point. ...Function calling part was super useful."

"It helped me understand how to implement RAG effectively."

Hands-on labs enhance practical application of concepts.

"The hands-on labs helped solidify my understanding."

"Loved the practical approach! Building the RAG system from scratch was the highlight."

"I learned how to use practical tools and strategies that I could apply immediately to my work."

May be fast-paced for absolute beginners to LLM APIs.

"My only minor feedback is that some parts felt a bit rushed, especially if you're entirely new to LLM APIs."

"As someone with more experience, I found some sections a bit basic, but it's probably great for beginners."

"I had some prior LLM knowledge, and this bridged the gap to Mistral perfectly."

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Getting Started with Mistral with these activities:

Organize and review your notes and assignments

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Compile and review your notes to enhance your understanding of the course materials and strengthen your knowledge retention.

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Gather your notes and assignments.
Organize your notes and assignments.
Review your notes and assignments.

Review the basics of natural language processing (NLP)

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Review the basics of NLP to refresh your memory and strengthen your understanding of the fundamental concepts that underpin Mistral AI's models.

Browse courses on Natural Language Processing

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Read articles and tutorials on NLP.
Watch videos and online courses on NLP.
Practice NLP techniques using online tools and resources.

Read 'Natural Language Processing with Python' by Steven Bird, Ewan Klein, and Edward Loper

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Read 'Natural Language Processing with Python' to supplement your learning and gain a deeper understanding of NLP, the foundation of Mistral AI's models.

View Natural Language Processing with Python:... on Amazon

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Read the book.
Take notes and highlight important concepts.
Complete the exercises in the book.

Four other activities

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Find a mentor who can provide guidance on using Mistral AI's models

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Find a mentor who can provide guidance and insights on using Mistral AI's models to enhance your learning and accelerate your progress.

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Identify potential mentors who have expertise in using Mistral AI.
Reach out to potential mentors.
Build a relationship with your mentor.

Build a chat interface to interact with the Mistral models

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Build a chat interface to interact with the Mistral models to gain hands-on experience and showcase your understanding of the models' capabilities.

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Design the chat interface.
Implement the chat interface.
Test the chat interface to ensure that it works correctly.

Practice using Mistral's native function calling

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Practice using Mistral's native function calling to build your skills and solidify your understanding of how to use LLMs to perform tasks that are better performed by traditional code.

Browse courses on Function Calling

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Identify a task that is better performed by traditional code.
Write the code to call the LLM tool.
Test the code to ensure that it works correctly.

Build a project that uses Mistral AI's models to solve a real-world problem

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Start a project to build something using Mistral AI's models that puts your skills to the test and deepens your understanding of the models' capabilities and practical applications.

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Identify a real-world problem that can be solved using Mistral AI's models.
Design and plan your project.
Implement your project.
Test and evaluate your project.

Career center

Learners who complete Getting Started with Mistral will develop knowledge and skills that may be useful to these careers:

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Getting Started with Mistral

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Practical guide to mistral ai models

Activities

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