Guidelines for Prompting
This page describes two prompting principles and their related tactics in order to write effective prompts for large language models.
Setup
Load the API key and relevant Python libaries.
In this course, we've provided some code that loads the OpenAI API key for you.
import openai
import os
from dotenv import load_dotenv, find_dotenv
_ = load_dotenv(find_dotenv())
openai.api_type = os.getenv("api_type")
openai.api_base = os.getenv("api_base")
openai.api_version = os.getenv("api_version")
openai.api_key = os.getenv("OPENAI_API_KEY")
Helper function
Throughout the discussion, we will use OpenAI's gpt-3.5-turbo model and the chat completions endpoint.
This helper function will make it easier to use prompts and look at the generated outputs:
def get_completion(prompt, model="chatgpt-gpt35-turbo"):
messages = [{"role": "user", "content": prompt}]
response = openai.ChatCompletion.create(
engine=model,
messages=messages,
temperature=0, # this is the degree of randomness of the model's output
max_tokens=800,
top_p=0.95,
frequency_penalty=0,
presence_penalty=0,
stop=None)
return response.choices[0].message["content"]
Prompting Principles
- Principle 1: Write clear and specific instructions
- Principle 2: Give the model time to “think”
Principle 1: Write clear and specific instructions
Tactic 1: Use delimiters to clearly indicate distinct parts of the input
- Delimiters can be anything like:
``, """, < >,,:`
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)
To guide a model towards the desired output and reduce the chances of irrelevant or incorrect responses, it is important to provide clear and specific instructions, which may require longer prompts for more clarity and context.
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)
[
{
"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 Life of Bees",
"author": "Lila Rose",
"genre": "Romance"
}
]
Tactic 3: Ask the model to check whether conditions are satisfied
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)
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!
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)
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)
<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)
Completion for prompt 1:
Two siblings, Jack and Jill, go on a quest to fetch water from a hilltop well, but misfortune strikes as they both fall down the hill, yet they return home slightly battered but with their adventurous spirits undimmed.
Deux frères et sœurs, Jack et Jill, partent en quête d'eau d'un puits au sommet d'une colline, mais ils tombent tous les deux et retournent chez eux légèrement meurtris mais avec leur esprit d'aventure intact.
Noms: Jack, Jill.
{
"french_summary": "Deux frères et sœurs, Jack et Jill, partent en quête d'eau d'un puits au sommet d'une colline, mais ils tombent tous les deux et retournent chez eux légèrement meurtris mais avec leur esprit d'aventure intact.",
"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)
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 la malchance frappe et ils dégringolent 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 la malchance frappe et ils dégringolent 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)
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:
Student's solution:
Actual solution:
Is the student's solution the same as actual solution \
just calculated:
Student grade:
Question:
Student's solution:
Actual solution:
"""
response = get_completion(prompt)
print(response)
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)
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 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 most stubborn stains and buildup, and it can help you achieve a brighter, healthier smile in just a few weeks.
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 can help you achieve a brighter, healthier smile in no time. Whether you're looking to improve your oral hygiene or simply want a more convenient and effective way to brush your teeth, this toothbrush is definitely worth considering.
A note about the backslash
- We are using a backslash
\to make the text fit on the screen without inserting newline '\n' characters. - GPT-3 isn't really affected whether you insert newline characters or not. But when working with LLMs in general, you may consider whether newline characters in your prompt may affect the model's performance.