The Path to Becoming an OpenAI Prompt Engineer: A Comprehensive Guide
9 min readFeb 5, 2024
Introduction:
A brief overview of what an OpenAI Prompt Engineer does. how to write a prompt for openai and explain why this role is increasingly important in AI.
Chapter 1: Understanding OpenAI and Prompt Engineering
Introduction to OpenAI as a leading research organization in the artificial intelligence industry. Explanation of what prompt engineering is instruction of AI model.
Chapter 2: Prerequisites and Skills
Discussion of the foundational skills required for prompt engineering, including:
- Proficiency in programming languages such as Python.
- Familiarity with machine learning concepts and frameworks (e.g., TensorFlow, PyTorch).
- Understanding of natural language processing (NLP) fundamentals.
- Highlighting the importance of critical thinking, creativity, and problem-solving skills.
Chapter 3: Educational Pathways
Overview of educational backgrounds commonly pursued by individuals entering prompt engineering roles, such as:
- Degrees in computer science, artificial intelligence, or related fields.
- Online courses and resources for learning machine learning and NLP.
- Specialized bootcamps or workshops focused on AI and deep learning.
Chapter 4: Lets start setup
Setup Load the OpenAI API Key and libraries.
- setup following way environment variable: (.env file)
OPENAI_API_KEY=sk.....or
import os
os.environ['OPENAI_API_KEY']='sk-....................'Load OpenAI API key in your workspace.
import openai
import os
from dotenv import load_dotenv, find_dotenv
_ = load_dotenv(find_dotenv())
openai.api_key = os.getenv('OPENAI_API_KEY')- If you are using OpenAI < 1.0.0 version
def chat_completion(prompt,model='gpt-3.5-turbo'):
messages = [{"role":"user","content":prompt}]
responce = openai.ChatCompetion.create(
model=model,
messages=messages,
temperature=0 # The degree of randomness of the model outpu
)
return responce.choices[0].message['content'] # only return output string- If you are using OpenAI > 1.0.0 version
def chat_completion(prompt,model='gpt-3.5-turbo'):
messages = [{"role":"user","content":prompt}]
responce = client.chat.competions.create(
model=model,
messages=messages,
temperature=0 # The degree of randomness of the model outpu
)
return responce.choices[0].message['content'] # only return output string- Check other openai models: Check List
Chapter 5: Prompting Principles
Openai prompting principles there are 2 types are:
- Principle 1: Write clear and specific instructions
- Principle 2: Give the model time to ‘think’
Tactics
Tactic 1: Use delimiters to indicate distinct parts of the input
- 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)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)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)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)Tactic 4: “Few-shot” prompting
prompt = f"""
Your task is to answer in a consistent style.
: Teach me about patience.
: 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.
: Teach me about resilience.
"""
response = get_completion(prompt)
print(response)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)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:
"""Tactic 2: Instruct the model to work out its 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/squarefoot - Icanbuysolarpanelsfor250 / square foot
- I negotiated a contract for maintenance that will cost \
me a flat 100kperyear, andanadditional10 / 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)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 including the final total.
- 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/squarefoot - Icanbuysolarpanelsfor250 / square foot
- I negotiated a contract for maintenance that will cost \
me a flat 100kperyear, andanadditional10 / 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)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)Try experimenting on your own!
Notes on using the OpenAI API outside of this classroom
To install the OpenAI Python library:
!pip install openaiThe 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-..."A note about the backslash
- In the course, we are using a backslash
\to make the text fit on the screen without inserting newline '\n' characters. - GPT-3 isn’t 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.
More about visit OpenAI Prompt Engineering, Deeplearning.ai
Chapter 6: Gaining Experience
Strategies for gaining practical experience in prompt engineering, including:
- Contributing to open-source projects related to NLP and machine learning.
- Participating in AI hackathons or competitions.
- Seeking internships or research assistant positions at organizations working on AI projects.
Chapter 7: Resources and Tools
A curated list of resources, tools, and platforms beneficial for aspiring prompt engineers, such as:
- Online courses (e.g., Coursera, Udacity, deeplearning) covering relevant topics.
- Books and research papers on NLP, machine learning, and AI ethics.
- AI development environments (e.g., Jupyter Notebook, Google Colab).
Chapter 8: Networking and Community Engagement
Importance of networking with professionals in the AI industry and participating in online communities, forums, and conferences. Suggestions for joining relevant groups on platforms like LinkedIn, GitHub, and Stack Overflow.
Chapter 9: Career Opportunities
Overview of potential career paths for prompt engineers, including roles in:
- Research and development at AI companies like OpenAI.
- Academia as researchers or professors.
- Government agencies or non-profit organizations focused on AI ethics and policy.
Conclusion:
Recap of key points discussed in the blog post. Encouragement for aspiring prompt engineers to pursue their passion and continue learning in this dynamic field. The above sample prompt get from deeplearning.ai thank you.
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