Getting Started with Mistral: Prompting and Model Selection with Mistral (Free AI Course – Part 2)

Welcome back to our series on mastering Mistral models! In the first article, we covered the basics of Mistral models, including setup and simple prompting techniques.

Now, we’re going to dive deeper into advanced features and model selection to help you unlock the full potential of these powerful tools.

• Introduction to Mistral Models and Setting Up Your Environment: Discover Mistral models and their capabilities, including setup and basic prompting techniques.
• Advanced Prompting and Model Selection with Mistral: Learn advanced prompting techniques, model selection criteria, and practical use case examples.
• Implementing Advanced Functionalities and Building Applications with Mistral: Explore function calling, Retrieval-Augmented Generation (RAG), and building interactive applications.

Choosing the right AI model for your task can make a huge difference. Mistral offers various models designed for different levels of complexity, from simple email classification to advanced reasoning and data analysis.

Understanding how to use these models effectively will enable you to create intelligent and efficient AI solutions.

In this article, we’ll explore advanced prompting techniques like personalization and summarization. We’ll also guide you on how to select the best Mistral model for your needs, ensuring you can tackle any AI challenge with confidence.

Advanced Prompting Techniques

Now that we have a good grasp of the basics, it’s time to explore some advanced prompting techniques.

These techniques will allow you to leverage Mistral models for more sophisticated tasks, making your AI applications more dynamic and effective. We’ll start with personalization, followed by summarization.

Personalization

Personalization is about tailoring responses to fit the specific context or user, making interactions feel more natural and engaging.

Let’s consider a scenario where you are a customer service bot for an online bookstore. You need to craft personalized email responses to customer inquiries.

Here’s an example prompt for personalizing an email response:

email = """
Dear bookstore team, 
I am interested in buying a new mystery novel. Can you recommend some popular titles available in your store?

Thank you,
Emily
"""

prompt = f"""
You are a bookstore customer service bot, and your task is to create personalized email responses to address customer questions.
Answer the customer's inquiry using the provided book list below. Ensure that your response is clear, friendly, and directly addresses the customer's question. Sign the email with "Bookstore Customer Support."   

# Book List
- "The Silent Patient" by Alex Michaelides
- "Gone Girl" by Gillian Flynn
- "Big Little Lies" by Liane Moriarty
- "The Girl with the Dragon Tattoo" by Stieg Larsson
- "In the Woods" by Tana French

# Email
{email}
"""
response = mistral(prompt)
print(response)

In this example, the bot uses a provided list of popular mystery novels to craft a personalized and friendly response to Emily’s inquiry. This approach makes the interaction feel more tailored and attentive.

Summarization

Summarization is a useful technique for condensing long pieces of text into shorter, more digestible summaries. It’s particularly handy for keeping up with lengthy reports or articles. Here’s how you can prompt Mistral to summarize a company newsletter:

newsletter = """
Our company has had a fantastic quarter with significant achievements across various departments. The sales team surpassed their targets by 20%, the marketing team successfully launched a new campaign that increased our social media engagement by 35%, and the product development team released two new features that received excellent customer feedback. Additionally, we have formed strategic partnerships with three industry-leading firms to expand our market reach.
"""

prompt = f"""
You are a content summarizer bot. Your task is to write a brief and clear summary of the provided newsletter, highlighting the main achievements and updates.

# Newsletter: 
{newsletter}
"""
response = mistral(prompt)
print(response)

In this prompt, the model is asked to condense the key points of the newsletter into a concise summary, making it easier to digest the important information quickly.

Criteria for Model Selection

Choosing the fitting model for your specific needs is essential for maximizing performance and obtaining optimal results. Mistral provides a range of models, each designed to manage varying degrees of task complexity.

By understanding these models and their best use cases, you can select the most suitable one for your tasks. This guide will assist you in navigating the available options to make the best choice for your needs.

Mistral Small

• Use Case: Simple tasks that require fast responses and lower computational costs.
• Example Task: Classifying customer emails as spam or not spam.

prompt = """
Classify the following email to determine if it is spam or not.
Only respond with the exact text "Spam" or "Not Spam".

# Email:
Congratulations! You've been selected to win a free trip to the Bahamas! Click the link to claim your prize.
"""
response = mistral(prompt, model="mistral-small-latest")
print(response)

In this scenario, the model quickly identifies whether an email is spam, providing a swift and cost-effective solution for basic classification tasks.

Mistral Medium

• Use Case: Intermediate tasks that involve more complexity, such as language transformation and content generation.
• Example Task: Composing a thank-you email for a recent purchase.

prompt = """
Compose a thank-you email for a customer who has just made their first purchase. 
Start by expressing gratitude for their business and then mention the product they purchased. 
Include relevant details about their order and sign the email with "The Awesome Store Team".

Order details:
- Customer name: John
- Product: wireless headphones
- Estimated delivery date: July 20, 2024
- Return policy: 30 days
"""
response_medium = mistral(prompt, model="mistral-medium-latest")
print(response_medium)

Here, the model creates a personalized thank-you email, showing its capability to handle more detailed and context-rich tasks.

Mistral Large

• Use Case: Complex tasks requiring advanced reasoning, detailed analysis, and handling large datasets.
• Example Task: Analyzing a dataset to find the customer with the closest payment amounts and calculate the difference in their payment dates.

prompt = """
Calculate the difference in payment dates between the two customers whose payment amounts are closest to each other in the following dataset. Do not write code.

# Dataset:
'{
  "transaction_id":{"0":"T2001","1":"T2002","2":"T2003","3":"T2004","4":"T2005"},
  "customer_id":{"0":"C101","1":"C102","2":"C103","3":"C102","4":"C101"},
  "payment_amount":{"0":250.5,"1":189.99,"2":220.0,"3":154.3,"4":310.2},
  "payment_date":{"0":"2022-01-05","1":"2022-01-06","2":"2022-01-07","3":"2022-01-05","4":"2022-01-08"},
  "payment_status":{"0":"Paid","1":"Unpaid","2":"Paid","3":"Paid","4":"Pending"}
}'
"""
response_large = mistral(prompt, model="mistral-large-latest")
print(response_large)

This example demonstrates the model’s ability to perform complex reasoning and data analysis, making it ideal for high-level tasks that require detailed processing.

Expense Reporting Task

For categorizing expenses based on transaction details, different models can be used depending on the complexity required:

transactions = """
Starbucks: 5.75
Whole Foods: 35.00
Amazon: 15.99
Chipotle: 12.50
Walmart: 47.89
Best Buy: 200.00
"""

prompt = f"""
Given the purchase details, categorize each expense into the following categories:
1) Food & Beverages
2) Groceries
3) Electronics
{transactions}
"""
response_small = mistral(prompt, model="mistral-small-latest")
print(response_small)
response_large = mistral(prompt, model="mistral-large-latest")
print(response_large)

Writing and Checking Code

For tasks that involve coding, such as writing a function based on given requirements, Mistral Large can be particularly useful:

user_message = """
Given an array of integers nums and an integer target, return indices of the two numbers such that they add up to the target.

You may assume that each input would have exactly one solution, and you may not use the same element twice.

You can return the answer in any order.
Your code should pass these tests:

assert twoSum([2,7,11,15], 9) == [0,1]
assert twoSum([3,2,4], 6) == [1,2]
assert twoSum([3,3], 6) == [0,1]
"""
print(mistral(user_message, model="mistral-large-latest"))

Use Case Examples for Different Models

To get the best results from Mistral models, it’s crucial to match the right model to the complexity of your task. Mistral offers models designed for different levels of difficulty, ensuring optimal performance for your specific needs.

Let’s look at some practical examples of how to use different Mistral models for tasks of varying complexity.

Simple Tasks:

• Classifying emails: Quickly determine whether an email is spam or not.
• Basic question answering: Provide straightforward answers to common questions.

Intermediate Tasks:

• Summarization: Condense lengthy articles or reports into concise summaries.
• Language transformation: Rewrite or translate text while maintaining its original meaning.

Complex Tasks:

• Advanced reasoning: Solve complex problems that require detailed analysis and logical thinking.
• Multi-step problem-solving: Handle tasks that involve multiple steps and dependencies.
• Detailed data analysis: Analyze large datasets to extract meaningful insights.

Understanding these use cases will help you choose the right Mistral model to optimize your workflow and achieve efficient, effective outcomes for your AI applications.

Final Thoughts

By understanding the strengths and ideal use cases for each Mistral model, you can confidently choose the one that best fits your needs. This will help you optimize performance and achieve the best possible results for your AI applications.

Recap of Key Points:

• Personalization: We explored how to create personalized email responses using Mistral models, enhancing customer interactions by tailoring responses to individual inquiries.
• Summarization: We demonstrated how to condense long texts into concise summaries, making it easier to extract key information quickly.
• Criteria for Model Selection: We discussed how to choose the right Mistral model based on the complexity of your tasks, ensuring optimal performance and efficiency.

With these advanced prompting techniques and criteria for model selection, you are now equipped to leverage Mistral models for a wide range of applications, from simple tasks to complex problem-solving. Let’s unlock the full potential of these models and take your AI projects to the next level.

What’s Next

In the next article, we will delve into implementing advanced functionalities and building applications with Mistral models. We will explore function calling, Retrieval-Augmented Generation (RAG), and the steps to build interactive applications. Stay tuned as we continue our journey to mastering Mistral models and unlocking even greater capabilities!