ChatGPT Promt Engineering for Developers and Promt Best practices - Course Part 2

Welcome back to the course "ChatGPT Promt Engineering for Developers" part 2 article.

In this article, we will mainly focus on prompt engineering best practices with examples. We will also learn principles for how to write prompts?to prompt engineer effectively.

If you have not checked part 1, I request to check it before you learn best practices - https://www.dhirubhai.net/pulse/chatgpt-promt-engineering-developers-course-part-1-pathan

We will also try some hands-on Jupyter Notebook exercises. In Jupyter Notebook examples, I'd also?encourage you to run the code yourself, so you can see what this output?is like and even change the exact prompts and play with a?few different variations to gain experience with what the?inputs and outputs of prompting are like.?

We are going to cover following principals and tactics in this article

Prompting Principles

Principle 1: Write clear and specific instructions

Tactic 1: Use delimiters to clearly indicate distinct parts of the input

Tactic 2: Ask for a structured output

Tactic 3: Ask the model to check whether conditions are satisfied

Tactic 4: "Few-shot" prompting

Principle 2: Give the model time to “think”

Tactic 1: Specify the steps required to complete a task

Tactic 2: Instruct the model to work out its own solution before rushing to a conclusion

Principal 3: Understand Model Limitations: Hallucinations

We will use the OpenAI Python library to access the?OpenAI API.?You need to setup the secret key in your OpenAI account to test the prompts.

We will use LLM GPT 3.5 Turbo model for testing the prompts.

Prompting principals will guide the model towards?the desired output and reduce the chance that you get irrelevant?or incorrect responses.

Don't confuse writing a clear prompt with writing a?short prompt because in many cases, longer prompts provide more clarity and context for the model, which leads to more detailed?and relevant outputs.

The first tactic to help you write clear?and specific instructions is to use delimiters to indicate distinct parts of the input clearly.

We have a paragraph, and the task we want to achieve?is summarizing this paragraph using GPT model. So, in the prompt, I've summarized the text delimited by triple backticks into?a single sentence. And then we have this kind?of triple backticks that are enclosing the text.?

Full code of the course will be available here - https://github.com/karimkhanp/ChatGPT-Prompt-Engineering-for-Developers/tree/master

To install the OpenAI Python library:

!pip install openai        

The library needs to be configured with your account's secret key, which is available on the?website.

You can either set it as the?OPENAI_API_KEY?environment variable before using the library:

 !export OPENAI_API_KEY='sk-...'        

Or, set?openai.api_key?to its value:

import openai
openai.api_key = "sk-..."        

Import required packages and OpenAI API key.

import openai
import os


from dotenv import load_dotenv, find_dotenv
_ = load_dotenv(find_dotenv())


openai.api_key? = os.getenv('OPENAI_API_KEY')i        

Throughout this course, we will use OpenAI's?gpt-3.5-turbo?model and the?chat completions endpoint.

def get_completion(prompt, model="gpt-3.5-turbo")
? ? messages = [{"role": "user", "content": prompt}]
? ? response = openai.ChatCompletion.create(
? ? ? ? model=model,
? ? ? ? messages=messages,
? ? ? ? temperature=0, # this is the degree of randomness of the model's output
? ? )
? ? return response.choices[0].message["content"]:        

Tactic 1: Use delimiters to indicate distinct parts of the input clearly

Delimiters can be anything like: ```, """, < >, `<tag> </tag>`, `:`

text = f""
You should express what you want a model to do by \?
providing instructions that are as clear and \?
specific as you can possibly make them. \?
This will guide the model towards the desired output, \?
and reduce the chances of receiving irrelevant \?
or incorrect responses. Don't confuse writing a \?
clear prompt with writing a short prompt. \?
In many cases, longer prompts provide more clarity \?
and context for the model, which can lead to \?
more detailed and relevant outputs.
"""
prompt = f"""
Summarize the text delimited by triple backticks \?
into a single sentence.
```{text}```
"""
response = get_completion(prompt)
print(response)"        

Output :

Clear and specific instructions should be provided to guide a model towards the desired output, and longer prompts can provide more clarity and context for the model, leading to more detailed and relevant outputs.        

Tactic 2: Ask for a structured output

- JSON, HTML

prompt = f""
Generate a list of three made-up book titles along \?
with their authors and genres.?
Provide them in JSON format with the following keys:?
book_id, title, author, genre.
"""
response = get_completion(prompt)
print(response)"        

Output

  {
    "book_id": 1,
    "title": "The Lost City of Zorath",
    "author": "Aria Blackwood",
    "genre": "Fantasy"
  },
  {
    "book_id": 2,
    "title": "The Last Survivors",
    "author": "Ethan Stone",
    "genre": "Science Fiction"
  },
  {
    "book_id": 3,
    "title": "The Secret of the Haunted Mansion",
    "author": "Lila Rose",
    "genre": "Mystery"
  }
][        

Tactic 3: Ask the model to check whether conditions are satisfied

Example 1

text_1 = f""
Making a cup of tea is easy! First, you need to get some \?
water boiling. While that's happening, \?
grab a cup and put a tea bag in it. Once the water is \?
hot enough, just pour it over the tea bag. \?
Let it sit for a bit so the tea can steep. After a \?
few minutes, take out the tea bag. If you \?
like, you can add some sugar or milk to taste. \?
And that's it! You've got yourself a delicious \?
cup of tea to enjoy.
"""
prompt = f"""
You will be provided with text delimited by triple quotes.?
If it contains a sequence of instructions, \?
re-write those instructions in the following format:


Step 1 - ...
Step 2 - …
…
Step N - …


If the text does not contain a sequence of instructions, \?
then simply write \"No steps provided.\"


\"\"\"{text_1}\"\"\"
"""
response = get_completion(prompt)
print("Completion for Text 1:")
print(response)"        

Output

Completion for Text 1
Step 1 - Get some water boiling.
Step 2 - Grab a cup and put a tea bag in it.
Step 3 - Once the water is hot enough, pour it over the tea bag.
Step 4 - Let it sit for a bit so the tea can steep.
Step 5 - After a few minutes, take out the tea bag.
Step 6 - Add some sugar or milk to taste.
Step 7 - Enjoy your delicious cup of tea!:        

Example 2

text_2 = f""
The sun is shining brightly today, and the birds are \
singing. It's a beautiful day to go for a \?
walk in the park. The flowers are blooming, and the \?
trees are swaying gently in the breeze. People \?
are out and about, enjoying the lovely weather. \?
Some are having picnics, while others are playing \?
games or simply relaxing on the grass. It's a \?
perfect day to spend time outdoors and appreciate the \?
beauty of nature.
"""
prompt = f"""
You will be provided with text delimited by triple quotes.?
If it contains a sequence of instructions, \?
re-write those instructions in the following format:


Step 1 - ...
Step 2 - …
…
Step N - …


If the text does not contain a sequence of instructions, \?
then simply write \"No steps provided.\"


\"\"\"{text_2}\"\"\"
"""
response = get_completion(prompt)
print("Completion for Text 2:")
print(response)"        

Output

Completion for Text 2
No steps provided.:        

Tactic 4: "Few-shot" prompting

prompt = f""
Your task is to answer in a consistent style.


<child>: Teach me about patience.


<grandparent>: The river that carves the deepest \?
valley flows from a modest spring; the \?
grandest symphony originates from a single note; \?
the most intricate tapestry begins with a solitary thread.


<child>: Teach me about resilience.
"""
response = get_completion(prompt)
print(response)"        

Output

<grandparent>: Resilience is like a tree that bends with the wind but never breaks. It is the ability to bounce back from adversity and keep moving forward, even when things get tough. Just like a tree that grows stronger with each storm it weathers, resilience is a quality that can be developed and strengthened over time
        

Principle 2: Give the model time to “think”

Tactic 1: Specify the steps required to complete a task?

text = f""
In a charming village, siblings Jack and Jill set out on \?
a quest to fetch water from a hilltop \?
well. As they climbed, singing joyfully, misfortune \?
struck—Jack tripped on a stone and tumbled \?
down the hill, with Jill following suit. \?
Though slightly battered, the pair returned home to \?
comforting embraces. Despite the mishap, \?
their adventurous spirits remained undimmed, and they \?
continued exploring with delight.
"""
# example 1
prompt_1 = f"""
Perform the following actions:?
1 - Summarize the following text delimited by triple \
backticks with 1 sentence.
2 - Translate the summary into French.
3 - List each name in the French summary.
4 - Output a json object that contains the following \
keys: french_summary, num_names.


Separate your answers with line breaks.


Text:
```{text}```
"""
response = get_completion(prompt_1)
print("Completion for prompt 1:")
print(response)"        

Output

Completion for prompt 1:
Two siblings, Jack and Jill, go on a quest to fetch water from a well on a hilltop, but misfortune strikes and they both tumble down the hill, returning home slightly battered but with their adventurous spirits undimmed.

Deux frères et s?urs, Jack et Jill, partent en quête d'eau d'un puits sur une colline, mais un malheur frappe et ils tombent tous les deux de la colline, rentrant chez eux légèrement meurtris mais avec leurs esprits aventureux intacts. 
Noms: Jack, Jill.

{
  "french_summary": "Deux frères et s?urs, Jack et Jill, partent en quête d'eau d'un puits sur une colline, mais un malheur frappe et ils tombent tous les deux de la colline, rentrant chez eux légèrement meurtris mais avec leurs esprits aventureux intacts.",
  "num_names": 2
}
         

Ask for output in a specified format

prompt_2 = f""
Your task is to perform the following actions:?
1 - Summarize the following text delimited by?
? <> with 1 sentence.
2 - Translate the summary into French.
3 - List each name in the French summary.
4 - Output a json object that contains the?
? following keys: french_summary, num_names.


Use the following format:
Text: <text to summarize>
Summary: <summary>
Translation: <summary translation>
Names: <list of names in Italian summary>
Output JSON: <json with summary and num_names>


Text: <{text}>
"""
response = get_completion(prompt_2)
print("\nCompletion for prompt 2:")
print(response)"        

Output

Completion for prompt 2
Summary: Jack and Jill go on a quest to fetch water, but misfortune strikes and they tumble down the hill, returning home slightly battered but with their adventurous spirits undimmed. 
Translation: Jack et Jill partent en quête d'eau, mais un malheur frappe et ils tombent de la colline, rentrant chez eux légèrement meurtris mais avec leurs esprits aventureux intacts.
Names: Jack, Jill
Output JSON: {"french_summary": "Jack et Jill partent en quête d'eau, mais un malheur frappe et ils tombent de la colline, rentrant chez eux légèrement meurtris mais avec leurs esprits aventureux intacts.", "num_names": 2}:        

Tactic 2: Instruct the model to work out its own solution before rushing to a conclusion

prompt = f""
Determine if the student's solution is correct or not.


Question:
I'm building a solar power installation and I need \
?help working out the financials.?
- Land costs $100 / square foot
- I can buy solar panels for $250 / square foot
- I negotiated a contract for maintenance that will cost \?
me a flat $100k per year, and an additional $10 / square \
foot
What is the total cost for the first year of operations?
as a function of the number of square feet.


Student's Solution:
Let x be the size of the installation in square feet.
Costs:
1. Land cost: 100x
2. Solar panel cost: 250x
3. Maintenance cost: 100,000 + 100x
Total cost: 100x + 250x + 100,000 + 100x = 450x + 100,000
"""
response = get_completion(prompt)
print(response)"        

Output

The student's solution is correct        

Note that the student's solution is actually not correct.

We can fix this by instructing the model to work out its own solution first.

prompt = f""
Your task is to determine if the student's solution \
is correct or not.
To solve the problem do the following:
- First, work out your own solution to the problem.?
- Then compare your solution to the student's solution \?
and evaluate if the student's solution is correct or not.?
Don't decide if the student's solution is correct until?
you have done the problem yourself.


Use the following format:
Question:
```
question here
```
Student's solution:
```
student's solution here
```
Actual solution:
```
steps to work out the solution and your solution here
```
Is the student's solution the same as actual solution \
just calculated:
```
yes or no
```
Student grade:
```
correct or incorrect
```


Question:
```
I'm building a solar power installation and I need help \
working out the financials.?
- Land costs $100 / square foot
- I can buy solar panels for $250 / square foot
- I negotiated a contract for maintenance that will cost \
me a flat $100k per year, and an additional $10 / square \
foot
What is the total cost for the first year of operations \
as a function of the number of square feet.
```?
Student's solution:
```
Let x be the size of the installation in square feet.
Costs:
1. Land cost: 100x
2. Solar panel cost: 250x
3. Maintenance cost: 100,000 + 100x
Total cost: 100x + 250x + 100,000 + 100x = 450x + 100,000
```
Actual solution:
"""
response = get_completion(prompt)
print(response)"        

Output

Let x be the size of the installation in square feet

Costs:
1. Land cost: 100x
2. Solar panel cost: 250x
3. Maintenance cost: 100,000 + 10x

Total cost: 100x + 250x + 100,000 + 10x = 360x + 100,000

Is the student's solution the same as actual solution just calculated:
No

Student grade:
Incorrect.        

Model Limitations: Hallucinations

Boie is a real company, the product name is not real.

prompt = f""
Tell me about AeroGlide UltraSlim Smart Toothbrush by Boie
"""
response = get_completion(prompt)
print(response)"        

Output

The AeroGlide UltraSlim Smart Toothbrush by Boie is a high-tech toothbrush that uses advanced sonic technology to provide a deep and thorough clean. It features a slim and sleek design that makes it easy to hold and maneuver, and it comes with a range of smart features that help you optimize your brushing routine

One of the key features of the AeroGlide UltraSlim Smart Toothbrush is its advanced sonic technology, which uses high-frequency vibrations to break up plaque and bacteria on your teeth and gums. This technology is highly effective at removing even the toughest stains and buildup, leaving your teeth feeling clean and refreshed.

In addition to its sonic technology, the AeroGlide UltraSlim Smart Toothbrush also comes with a range of smart features that help you optimize your brushing routine. These include a built-in timer that ensures you brush for the recommended two minutes, as well as a pressure sensor that alerts you if you're brushing too hard.

Overall, the AeroGlide UltraSlim Smart Toothbrush by Boie is a highly advanced and effective toothbrush that is perfect for anyone looking to take their oral hygiene to the next level. With its advanced sonic technology and smart features, it provides a deep and thorough clean that leaves your teeth feeling fresh and healthy..        

This is an example of?where the model confabulates a description of a?made-up product name from a real toothbrush company.

So, the prompt?is, Tell me about AeroGlide Ultra Slim Smart?Toothbrush by Boie.?

So if we run this, the model is going to give us a?pretty realistic-sounding description of a fictitious product.?And the reason that this can be dangerous is that this actually sounds pretty?realistic.

So, make sure to kind of use?some of the techniques that we've gone through in this article?to try and kind of avoid this when we are building?oue own applications.?