Example - Create a ChatGPT prompt that would have created the following 
LinkedIn post: 

For marketers, using AI services can feel like turning on autopilot on a 
self-driving car.

With the right information, it can be very powerful.

Daniel Gilbert, Brainlabs Global CEO spoke to Richard Tyler at The Times 
on how AI can make online marketing more accurate.

“You could say to Google’s AI - ‘here’s my website and credit card’… and 
it will do a pretty poor job. But when the machine has all the 
information, it can do a great job of pushing to the right customers.”

Our proof?

💡 We tested Google’s Performance Max and achieved an 18% increase in 
customer conversions, and a 12% fall in cost.

🔗 Read the full article here: http://bit.ly/3Fg7klv


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Create a ChatGPT prompt that would have generated the following code:

# Create a data pipeline of 7 steps from a JSON file to a CSV file, using 
pandas:
import pandas as pd
import json
import openai


openai.api_key = ""


# Extract the reviews from a JSON file
with open("reviews.json") as f:
   reviews = json.load(f)


# Loop over all of the reviews and extract the classification labels using 
the OpenAI API:
for review in reviews:
   # Create the base prompt:
   base_prompt = "You are responsible for classifying text from TripAvisor 
for a restaurant.\nThe possible labels are happy or unhappy.\nBelow you'll 
find an example of the review text and output:\n---\nreviewText: This 
Mexican restaurant is truly a hidden gem. The flavors in every dish were 
so authentic and truly transported me to Mexico. I can’t wait to go back 
for more!\nlabel: happy\n---"
   response = openai.Completion.create(
   model="text-davinci-003",
   prompt=f"""{base_prompt}\nreviewText: 
{review['reviewText']}\nlabel:""",
   temperature=0.73,
   max_tokens=1339,
   top_p=1,
   frequency_penalty=0,
   presence_penalty=0
   )
   review["classification"] = response.choices[0].text.strip()


# Create a pandas DataFrame from the reviews:
df = pd.DataFrame(reviews)


# Save the DataFrame as a CSV file:
df.to_csv("reviews.csv", index=False)