We may earn an affiliate commission when you visit our partners.

Getting Started with Mistral

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.

Enroll now

Or subscribe to Coursera Plus

And get unlimited access to Coursera

Two deals to help you save

Save money when you learn. Here are two deals and offers that may be relevant to this course.

All coupon codes, vouchers, and discounts are applied automatically unless otherwise noted.

Valid until December 2

Annual Subscription

Invest in your career growth and save. Start your annual subscription to Coursera Plus and access 10,000+ programs.

Valid until March 12, 2027

Save 40% Off 3 Months

Access thousands of top-rated learning programs with a Coursera Plus monthly subscription and take 40% off 3 months for a limited time.

What's inside

Syllabus

Traffic lights

Read about what's good

what should give you pause

and possible dealbreakers

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

Save this course

Create your own learning path. Save this course to your list so you can find it easily later.

Save

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

Show steps

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

Show steps

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

Review the basics of natural language processing (NLP)

Show steps

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

Show steps

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

Show steps

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

Show steps

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

Four other activities

Expand to see all activities and additional details

Show all seven activities

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

Show steps

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

Show steps

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

Show steps

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

Show steps

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

Show steps

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

Show steps

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

Show steps

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.

Show steps

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:

Reading list

We've selected 50 books that we think will supplement your learning. Use these to develop background knowledge, enrich your coursework, and gain a deeper understanding of the topics covered in Getting Started with Mistral.

Speech and Language Processing. An Introduction to...

Save

This widely recognized and comprehensive textbook that provides a deep dive into the subject of language processing. It is suitable for both undergraduate and graduate-level courses and is considered a must-read for gaining a strong theoretical and applied understanding of NLP. The authors are leading researchers in the field, and the book is frequently updated to reflect advancements. The latest draft is available online.

Getting Started with Mistral

Two deals to help you save

What's inside

Syllabus

Traffic lights

Save this course

Reviews summary

Practical guide to mistral ai models

Activities

Career center

Reading list

Share

Similar courses

Coursera Buyer Protection & Guarantee

7-Day Free Trial & Free Audit

14-Day Refund Guarantee

Verified Academic & Industry Partner