Developing a Robust System Prompt for a Personal Tutor Using Flask

Developing a Robust System Prompt for a Personal Tutor

Welcome back! Building on your previous experience with the OpenAI SDK, this course will guide you in developing a RESTful API for a Personal Tutor using Flask . In this lesson, we begin by crafting a robust system prompt that provides the DeepSeek model with clear guidelines and ample context for effective educational interactions. You'll learn how to store this prompt in a file and seamlessly load it into your code, setting the foundation for a well-structured tutoring service.

Importance of a Robust System Prompt

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Loading the System Prompt in Python

Now that we understand the structure of a system prompt, let's see how to load it into our Python code. We will create a load_system_prompt function that accepts a file path as an argument. This function will read the system prompt from the specified file and return its content. If an error occurs while reading the file, a default prompt will be returned instead.

Here's the code for the load_system_prompt function:

Python

def load_system_prompt(file_path: str) -> str:
    """Load the tutor system prompt from file."""
    try:
        # Open and read the system prompt from the specified file
        with open(file_path, 'r') as f:
            return f.read()
    except Exception as e:
        # Print an error message if the file cannot be read
        print(f"Error loading system prompt: {e}")
        # Return a default prompt if there's an error
        return "You are a helpful tutor."

def load_system_prompt(file_path: str) -> str:
    """Load the tutor system prompt from file."""
    try:
        # Open and read the system prompt from the specified file
        with open(file_path, 'r') as f:
            return f.read()
    except Exception as e:
        # Print an error message if the file cannot be read
        print(f"Error loading system prompt: {e}")
        # Return a default prompt if there's an error
        return "You are a helpful tutor."

This function uses a try-except block to handle potential errors, such as the file not being found. If the file is successfully read, its content is returned. Otherwise, an error message is printed, and a default prompt is used.

To utilize this function, we load the system prompt into our code by passing the file path as follows:

Python

# Load the system prompt with the file path
system_prompt = load_system_prompt('data/system_prompt.txt')

# Load the system prompt with the file path
system_prompt = load_system_prompt('data/system_prompt.txt')

By loading the system prompt, we ensure that our tutor is equipped with the necessary context and guidelines to interact effectively with students.

Testing the System Prompt

Once the system prompt is loaded, it can be used as part of a conversation with a language model. The system prompt provides the model with the necessary context and instructions for acting as a tutor. To test the system prompt, you can create a conversation that includes the system prompt and a sample student question. The model will then generate a response based on the provided context.

A typical conversation structure might look like this:

Python

# Example conversation with the system prompt and a student question
conversation = [
    {"role": "system", "content": system_prompt},
    {"role": "user", "content": "Can you explain the basics of algebra?"}
]

# The conversation can then be sent to a language model for a response.
# The response will be generated based on the system prompt and the user's question.

# Example conversation with the system prompt and a student question
conversation = [
    {"role": "system", "content": system_prompt},
    {"role": "user", "content": "Can you explain the basics of algebra?"}
]

# The conversation can then be sent to a language model for a response.
# The response will be generated based on the system prompt and the user's question.

The conversation can then be sent to a language model for a response. The response will be generated based on the system prompt and the user's question.

To implement this, you can use the following code:

Python

from openai import OpenAI

# Initialize the DeepSeek client
client = OpenAI()

def send_query(messages):
    """Send a query and receive a tutoring explanation."""
    response = client.chat.completions.create(
        model="deepseek-ai/DeepSeek-V3",
        messages=messages
    )
    return response.choices[0].message.content.strip()

# Send the query and get a response
response = send_query(conversation)

# Print the student's question and the tutor's explanation
print(f"Student:\n{conversation[1]['content']}\n")
print(f"Tutor:\n{response}")

from openai import OpenAI

# Initialize the DeepSeek client
client = OpenAI()

def send_query(messages):
    """Send a query and receive a tutoring explanation."""
    response = client.chat.completions.create(
        model="deepseek-ai/DeepSeek-V3",
        messages=messages
    )
    return response.choices[0].message.content.strip()

# Send the query and get a response
response = send_query(conversation)

# Print the student's question and the tutor's explanation
print(f"Student:\n{conversation[1]['content']}\n")
print(f"Tutor:\n{response}")

Example Tutor Response

When the conversation is processed, the tutor uses the system prompt to generate a response. This example illustrates how the system prompt effectively guides the tutor's interaction with the student:

text

Student:
Can you explain the basics of algebra?

Tutor:
I'd be happy to explain the basics of algebra! Algebra is a branch of mathematics that helps us solve problems by using letters and symbols to represent numbers and relationships.

Let me break down the fundamentals:

1. **Variables**: These are letters (like x, y, z) that represent unknown values or quantities that can change. They're like containers that can hold different numbers.

2. **Expressions**: These are combinations of variables, numbers, and operations (like +, -, ×, ÷). For example: 3x + 4 or y² - 7.

3. **Equations**: These are statements that show two expressions are equal, connected by an equals sign (=). For example: x + 5 = 12.

4. **Solving equations**: This involves finding the value of the variable that makes the equation true. For the equation x + 5 = 12, we can solve for x:
   - Subtract 5 from both sides: x = 12 - 5
   - Therefore: x = 7

5. **Properties**:
   - Commutative property: a + b = b + a and a × b = b × a
   - Associative property: (a + b) + c = a + (b + c) and (a × b) × c = a × (b × c)
   - Distributive property: a × (b + c) = (a × b) + (a × c)

Algebra helps us:
- Solve real-world problems by converting them into mathematical equations
- Find unknown values
- Express patterns and relationships
- Make predictions based on existing data

What's your current knowledge level with algebra? Would you like me to explain any specific concept in more detail or provide some simple practice problems to help you understand better?

Student:
Can you explain the basics of algebra?

Tutor:
I'd be happy to explain the basics of algebra! Algebra is a branch of mathematics that helps us solve problems by using letters and symbols to represent numbers and relationships.

Let me break down the fundamentals:

1. **Variables**: These are letters (like x, y, z) that represent unknown values or quantities that can change. They're like containers that can hold different numbers.

2. **Expressions**: These are combinations of variables, numbers, and operations (like +, -, ×, ÷). For example: 3x + 4 or y² - 7.

3. **Equations**: These are statements that show two expressions are equal, connected by an equals sign (=). For example: x + 5 = 12.

4. **Solving equations**: This involves finding the value of the variable that makes the equation true. For the equation x + 5 = 12, we can solve for x:
   - Subtract 5 from both sides: x = 12 - 5
   - Therefore: x = 7

5. **Properties**:
   - Commutative property: a + b = b + a and a × b = b × a
   - Associative property: (a + b) + c = a + (b + c) and (a × b) × c = a × (b × c)
   - Distributive property: a × (b + c) = (a × b) + (a × c)

Algebra helps us:
- Solve real-world problems by converting them into mathematical equations
- Find unknown values
- Express patterns and relationships
- Make predictions based on existing data

What's your current knowledge level with algebra? Would you like me to explain any specific concept in more detail or provide some simple practice problems to help you understand better?

This response showcases the tutor's ability to provide detailed and relevant educational information based on the system prompt, ensuring a consistent and informative learning experience. Notice how the tutor:

Breaks down the complex topic into manageable parts
Uses clear explanations for each concept
Provides examples to illustrate key points
Ends with a question to assess the student's understanding

These behaviors directly reflect the guidelines provided in the system prompt.