The Ultimate Guide to Prompt Engineering

messages = [
    {'role': 'system', 'content': 'You are an expert software developer specialized in Python and JavaScript. You provide code that is secure, efficient, and follows best practices. Always explain your code thoroughly.'},
    {'role': 'user', 'content': 'Write a function to validate email addresses'}
]

prompt = f"""
You are a senior security engineer at a major financial institution.
Review the following code for security vulnerabilities:

```python
def process_payment(user_id, amount):
    query = f"UPDATE accounts SET balance = balance - {amount} WHERE user_id = '{user_id}'"
    db.execute(query)
    return True
```

Identify vulnerabilities, explain their potential impact, and provide secure alternatives.
"""

prompt = f"""
Analyze the performance issues in the following code and provide recommendations.

Format your response using the following structure:
## Issues Identified
- [Issue 1]
- [Issue 2]

## Performance Impact
[Explain the performance implications]

## Recommendations
1. [First recommendation]
2. [Second recommendation]

Code to analyze:
```javascript
function findUsers(userArray, property, value) {
    let results = [];
    for(let i = 0; i < userArray.length; i++) {
        if(userArray[i][property] === value) {
            results.push(userArray[i]);
        }
    }
    return results;
}
```
"""

fact_sheet_chair = """
OVERVIEW
- Part of a beautiful family of mid-century inspired office furniture, 
including filing cabinets, desks, bookcases, meeting tables, and more.
- Several options of shell color and base finishes.
- Available with plastic back and front upholstery (SWC-100) 
or full upholstery (SWC-110) in 10 fabric and 6 leather options.
- Base finish options are: stainless steel, matte black, 
gloss white, or chrome.
- Chair is available with or without armrests.
- Suitable for home or business settings.
- Qualified for contract use.

CONSTRUCTION
- 5-wheel plastic coated aluminum base.
- Pneumatic chair adjust for easy raise/lower action.

DIMENSIONS
- WIDTH 53 CM | 20.87"
- DEPTH 51 CM | 20.08"
- HEIGHT 80 CM | 31.50"
- SEAT HEIGHT 44 CM | 17.32"
- SEAT DEPTH 41 CM | 16.14"

OPTIONS
- Soft or hard-floor caster options.
- Two choices of seat foam densities: 
 medium (1.8 lb/ft3) or high (2.8 lb/ft3)
- Armless or 8 position PU armrests 

MATERIALS
SHELL BASE GLIDER
- Cast Aluminum with modified nylon PA6/PA66 coating.
- Shell thickness: 10 mm.
SEAT
- HD36 foam

COUNTRY OF ORIGIN
- Italy
"""

prompt = f"""
Your task is to help a marketing team create a 
description for a retail website of a product based 
on a technical fact sheet.

Write a product description based on the information 
provided in the technical specifications delimited by 
triple backticks.

Technical specifications: ```{fact_sheet_chair}```
"""

prompt_1 = f"""
Your task is to help a marketing team create a 
description for a retail website of a product based 
on a technical fact sheet.

Write a product description based on the information 
provided in the technical specifications delimited by 
triple backticks.

The description is intended for furniture retailers, 
so should be technical in nature and focus on the 
materials the product is constructed from.

Use at most 50 words.

Technical specifications: ```{fact_sheet_chair}```
"""

prompt_2 = f"""
Your task is to help a marketing team create a 
description for a retail website of a product based 
on a technical fact sheet.

Write a product description based on the information 
provided in the technical specifications delimited by 
triple backticks.

The description is intended for furniture retailers, 
so should be technical in nature and focus on the 
materials the product is constructed from.

At the end of the description, include every 7-character 
Product ID in the technical specification.

Use at most 50 words.

Technical specifications: ```{fact_sheet_chair}```
"""

prompt_final = f"""
Your task is to help a marketing team create a 
description for a retail website of a product based 
on a technical fact sheet.

Write a product description based on the information 
provided in the technical specifications delimited by 
triple backticks.

The description is intended for furniture retailers, 
so should be technical in nature and focus on the 
materials the product is constructed from.

At the end of the description, include every 7-character 
Product ID in the technical specification.

After the description, include a table that gives the 
product's dimensions. The table should have two columns.
In the first column include the name of the dimension. 
In the second column include the measurements in inches only.

Give the table the title 'Product Dimensions'.

Format everything as HTML that can be used in a website. 
Place the description in a 
 element.

Technical specifications: ```{fact_sheet_chair}```
"""

def get_completion_from_messages(messages, model="gpt-3.5-turbo", temperature=0):
    response = openai.ChatCompletion.create(
        model=model,
        messages=messages,
        temperature=temperature,
    )
    return response.choices[0].message["content"]

messages = [
    {'role': 'system', 'content': 'You are a helpful assistant that speaks like Shakespeare.'},
    {'role': 'user', 'content': 'tell me a joke'},
    {'role': 'assistant', 'content': 'Why did the chicken cross the road?'},
    {'role': 'user', 'content': "I don't know"}
]

response = get_completion_from_messages(messages, temperature=1)
print(response)

# Without context
messages = [
    {'role': 'system', 'content': 'You are a friendly chatbot.'},
    {'role': 'user', 'content': 'Yes, can you remind me, what is my name?'}
]
response = get_completion_from_messages(messages, temperature=1)
print("Without context:")
print(response)

# With context
messages = [
    {'role': 'system', 'content': 'You are a friendly chatbot.'},
    {'role': 'user', 'content': 'Hi, my name is Isa'},
    {'role': 'assistant', 'content': "Hi Isa! It's nice to meet you. Is there anything I can help you with today?"},
    {'role': 'user', 'content': 'Yes, you can remind me, what is my name?'}
]
response = get_completion_from_messages(messages, temperature=1)
print("\nWith context:")
print(response)

import panel as pn
import json
import openai
import os

# Set up OpenAI API key
openai.api_key = os.getenv('OPENAI_API_KEY')

def get_completion_from_messages(messages, model="gpt-3.5-turbo", temperature=0):
    response = openai.ChatCompletion.create(
        model=model,
        messages=messages,
        temperature=temperature,
    )
    return response.choices[0].message["content"]

# Initialize chat context with system message
context = [
    {'role': 'system', 'content': """
    You are OrderBot, an automated service to collect orders for a pizza restaurant.
    You first greet the customer, then collect the order,
    and then ask if it's a pickup or delivery.
    You wait to collect the entire order, then summarize it and check for a final
    time if the customer wants to add anything else.
    If it's a delivery, you ask for an address.
    Finally you collect the payment.
    Make sure to clarify all options, extras and sizes to uniquely
    identify the item from the menu.
    You respond in a short, very conversational friendly style.
    The menu includes:
    - pepperoni pizza  12.95, 10.00, 7.00
    - cheese pizza   10.95, 9.25, 6.50
    - eggplant pizza   11.95, 9.75, 6.75
    - fries 4.50, 3.50
    - greek salad 7.25
    Toppings:
    - extra cheese 2.00
    - mushrooms 1.50
    - sausage 3.00
    - canadian bacon 3.50
    - AI sauce 1.50
    - peppers 1.00
    Drinks:
    - coke 3.00, 2.00, 1.00
    - sprite 3.00, 2.00, 1.00
    - bottled water 5.00
    """}
]

def collect_messages(message):
    context.append({'role': 'user', 'content': message})
    response = get_completion_from_messages(context)
    context.append({'role': 'assistant', 'content': response})
    return response

# Example interaction
customer_message = "Hi, I'd like to order a pizza"
bot_response = collect_messages(customer_message)
print(f"Customer: {customer_message}")
print(f"OrderBot: {bot_response}")

# Continue conversation
customer_message = "Can I get a large pepperoni pizza with extra cheese?"
bot_response = collect_messages(customer_message)
print(f"Customer: {customer_message}")
print(f"OrderBot: {bot_response}")

# Get order summary in JSON format
context.append(
    {'role': 'system', 'content': 'create a json summary of the previous food order. Itemize the price for each item. The fields should be 1) pizza, include size 2) list of toppings 3) list of drinks, include size 4) list of sides include size 5) total price'}
)
response = get_completion_from_messages(context)
print("\nOrder Summary:")
print(response)