ChatGPT Prompt Engineering for Developers

Cell 2023/5/1 LLMChatGPTPrompt

# Introduction

# Two Types of large language models(LLMs)

Base LLM
- Predict next word, based on text training data
Instruction Tuned LLM
- try to follow instructions
- Fine-tune on instructions and good attempts at following those instructions
- RLHF: Reinforcement Learning with Human Feedback

# Guidelines for Prompting

# Init Environment

Load the API key and relevant Python libaries.

import openai
import os

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

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

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This helper function will make it easier to use prompts and look at the generated outputs:

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"]

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# Prompting Principles

# Write clear and specific instructions

Use delimiters to clearly indicate distinct parts of the input

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

example:

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)

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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.

Ask for a structured output

JSON, XML, HTML, etc.

example:

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)

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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 Life of Bees",
    "author": "Lila Rose",
    "genre": "Romance"
  }
]

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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)

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output 1:

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!

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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)

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output 2:

Completion for Text 2:
No steps provided.

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"Few-shot" prompting

Give successful examples of completing tasks, then ask model to perform the task

example:

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)

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output:

&lt;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.

# Give the model time to “think”

Specify the steps required to complete a task

example:

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)

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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 sœurs, 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 sœurs, 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
}

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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)

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output:

Completion for prompt 2:
Text: 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.
Summary: 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.
Translation: Deux frères et sœurs, 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.
Names: Jack, Jill
Output JSON: {
  "summary": "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.",
  "translation": "Deux frères et sœurs, 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.",
  "names": [
    "Jack",
    "Jill"
  ]
}

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

example:

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)

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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
```

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# Model Limitations: Hallucinations

When the model is asked to generate text, it will sometimes hallucinate text that is not present in the prompt. This is a known limitation of the model. The model is trained to generate text that is similar to the text in the prompt, but it is not trained to generate text that is semantically consistent with the prompt. This means that the model may generate text that is not true or that is inconsistent with the prompt.

Reducing hallucinations

First find relevant information, then answer the question based on the relevant information.

Note the backslash

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.

# Iterative Prompt Development

# Prompt Guidelines

Be clear and specific
Analyze why result does not give desired output
Refine the idea and the prompt
Repeat

# Iterative Process

Try something
Analyze where the result does not give what you want
Clarify instructions, give more space and time to think
Refine prompts with a batch of examples

# Summarizing

Text to summarize:

prod_review = """
Got this panda plush toy for my daughter's birthday, \
who loves it and takes it everywhere. It's soft and \
super cute, and its face has a friendly look. It's \
a bit small for what I paid though. I think there \
might be other options that are bigger for the \
same price. It arrived a day earlier than expected, \
so I got to play with it myself before I gave it \
to her.
"""

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# Summarize with a word/sentence/character limit

prompt = f"""
Your task is to generate a short summary of a product \
review from an ecommerce site.

Summarize the review below, delimited by triple
backticks, in at most 30 words.

Review: ```{prod_review}```
"""

response = get_completion(prompt)
print(response)

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output:

Soft and cute panda plush toy loved by daughter, but a bit small for the price. Arrived early.

# Summarize with a focus on shipping and delivery

prompt = f"""
Your task is to generate a short summary of a product \
review from an ecommerce site to give feedback to the \
Shipping deparmtment.

Summarize the review below, delimited by triple
backticks, in at most 30 words, and focusing on any aspects \
that mention shipping and delivery of the product.

Review: ```{prod_review}```
"""

response = get_completion(prompt)
print(response)

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output:

The panda plush toy arrived a day earlier than expected, but the customer felt it was a bit small for the price paid.

# Summarize with a focus on price and value

prompt = f"""
Your task is to generate a short summary of a product \
review from an ecommerce site to give feedback to the \
pricing deparmtment, responsible for determining the \
price of the product.

Summarize the review below, delimited by triple
backticks, in at most 30 words, and focusing on any aspects \
that are relevant to the price and perceived value.

Review: ```{prod_review}```
"""

response = get_completion(prompt)
print(response)

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output:

The panda plush toy is soft, cute, and loved by the recipient, but the price may be too high for its size.

# Try "extract" instead of "summarize"

prompt = f"""
Your task is to extract relevant information from \
a product review from an ecommerce site to give \
feedback to the Shipping department.

From the review below, delimited by triple quotes \
extract the information relevant to shipping and \
delivery. Limit to 30 words.

Review: ```{prod_review}```
"""

response = get_completion(prompt)
print(response)

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output:

The product arrived a day earlier than expected.

# Summarize multiple product reviews

review_1 = prod_review

# review for a standing lamp
review_2 = """
Needed a nice lamp for my bedroom, and this one \
had additional storage and not too high of a price \
point. Got it fast - arrived in 2 days. The string \
to the lamp broke during the transit and the company \
happily sent over a new one. Came within a few days \
as well. It was easy to put together. Then I had a \
missing part, so I contacted their support and they \
very quickly got me the missing piece! Seems to me \
to be a great company that cares about their customers \
and products.
"""

# review for an electric toothbrush
review_3 = """
My dental hygienist recommended an electric toothbrush, \
which is why I got this. The battery life seems to be \
pretty impressive so far. After initial charging and \
leaving the charger plugged in for the first week to \
condition the battery, I've unplugged the charger and \
been using it for twice daily brushing for the last \
3 weeks all on the same charge. But the toothbrush head \
is too small. I’ve seen baby toothbrushes bigger than \
this one. I wish the head was bigger with different \
length bristles to get between teeth better because \
this one doesn’t.  Overall if you can get this one \
around the $50 mark, it's a good deal. The manufactuer's \
replacements heads are pretty expensive, but you can \
get generic ones that're more reasonably priced. This \
toothbrush makes me feel like I've been to the dentist \
every day. My teeth feel sparkly clean!
"""

# review for a blender
review_4 = """
So, they still had the 17 piece system on seasonal \
sale for around $49 in the month of November, about \
half off, but for some reason (call it price gouging) \
around the second week of December the prices all went \
up to about anywhere from between $70-$89 for the same \
system. And the 11 piece system went up around $10 or \
so in price also from the earlier sale price of $29. \
So it looks okay, but if you look at the base, the part \
where the blade locks into place doesn’t look as good \
as in previous editions from a few years ago, but I \
plan to be very gentle with it (example, I crush \
very hard items like beans, ice, rice, etc. in the \
blender first then pulverize them in the serving size \
I want in the blender then switch to the whipping \
blade for a finer flour, and use the cross cutting blade \
first when making smoothies, then use the flat blade \
if I need them finer/less pulpy). Special tip when making \
smoothies, finely cut and freeze the fruits and \
vegetables (if using spinach-lightly stew soften the \
spinach then freeze until ready for use-and if making \
sorbet, use a small to medium sized food processor) \
that you plan to use that way you can avoid adding so \
much ice if at all-when making your smoothie. \
After about a year, the motor was making a funny noise. \
I called customer service but the warranty expired \
already, so I had to buy another one. FYI: The overall \
quality has gone done in these types of products, so \
they are kind of counting on brand recognition and \
consumer loyalty to maintain sales. Got it in about \
two days.
"""

reviews = [review_1, review_2, review_3, review_4]

for i in range(len(reviews)):
    prompt = f"""
    Your task is to generate a short summary of a product \
    review from an ecommerce site.

    Summarize the review below, delimited by triple \
    backticks in at most 20 words.

    Review: ```{reviews[i]}```
    """

    response = get_completion(prompt)
    print(i, response, "\n")

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output:

0 Soft and cute panda plush toy loved by daughter, but a bit small for the price. Arrived early.

1 Affordable lamp with storage, fast shipping, and excellent customer service. Easy to assemble and missing parts were quickly replaced.

2 Good battery life, small toothbrush head, but effective cleaning. Good deal if bought around $50.

3 Mixed review of a blender system with price gouging and decreased quality, but helpful tips for use.

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# Inferring

Product review text:

lamp_review = """
Needed a nice lamp for my bedroom, and this one had \
additional storage and not too high of a price point. \
Got it fast.  The string to our lamp broke during the \
transit and the company happily sent over a new one. \
Came within a few days as well. It was easy to put \
together.  I had a missing part, so I contacted their \
support and they very quickly got me the missing piece! \
Lumina seems to me to be a great company that cares \
about their customers and products!!
"""

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# Sentiment (positive/negative)

prompt = f"""
What is the sentiment of the following product review,
which is delimited with triple backticks?

Review text: '''{lamp_review}'''
"""
response = get_completion(prompt)
print(response)

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output:

The sentiment of the product review is positive.

Using a single word:

prompt = f"""
What is the sentiment of the following product review,
which is delimited with triple backticks?

Give your answer as a single word, either "positive" \
or "negative".

Review text: '''{lamp_review}'''
"""
response = get_completion(prompt)
print(response)

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output:

positive

# Identify types of emotions

prompt = f"""
Identify a list of emotions that the writer of the \
following review is expressing. Include no more than \
five items in the list. Format your answer as a list of \
lower-case words separated by commas.

Review text: '''{lamp_review}'''
"""
response = get_completion(prompt)
print(response)

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output:

happy, satisfied, grateful, impressed, content

# Identify anger

prompt = f"""
Is the writer of the following review expressing anger?\
The review is delimited with triple backticks. \
Give your answer as either yes or no.

Review text: '''{lamp_review}'''
"""
response = get_completion(prompt)
print(response)

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output:

No

# Extract product and company name from customer reviews

prompt = f"""
Identify the following items from the review text:
- Item purchased by reviewer
- Company that made the item

The review is delimited with triple backticks. \
Format your response as a JSON object with \
"Item" and "Brand" as the keys.
If the information isn't present, use "unknown" \
as the value.
Make your response as short as possible.

Review text: '''{lamp_review}'''
"""
response = get_completion(prompt)
print(response)

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output:

{
  "Item": "lamp",
  "Brand": "Lumina"
}

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# Doing multiple tasks at once

prompt = f"""
Identify the following items from the review text:
- Sentiment (positive or negative)
- Is the reviewer expressing anger? (true or false)
- Item purchased by reviewer
- Company that made the item

The review is delimited with triple backticks. \
Format your response as a JSON object with \
"Sentiment", "Anger", "Item" and "Brand" as the keys.
If the information isn't present, use "unknown" \
as the value.
Make your response as short as possible.
Format the Anger value as a boolean.

Review text: '''{lamp_review}'''
"""
response = get_completion(prompt)
print(response)

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output:

{
  "Sentiment": "positive",
  "Anger": false,
  "Item": "lamp",
  "Brand": "Lumina"
}

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# Inferring topics

story = """
In a recent survey conducted by the government,
public sector employees were asked to rate their level
of satisfaction with the department they work at.
The results revealed that NASA was the most popular
department with a satisfaction rating of 95%.

One NASA employee, John Smith, commented on the findings,
stating, "I'm not surprised that NASA came out on top.
It's a great place to work with amazing people and
incredible opportunities. I'm proud to be a part of
such an innovative organization."

The results were also welcomed by NASA's management team,
with Director Tom Johnson stating, "We are thrilled to
hear that our employees are satisfied with their work at NASA.
We have a talented and dedicated team who work tirelessly
to achieve our goals, and it's fantastic to see that their
hard work is paying off."

The survey also revealed that the
Social Security Administration had the lowest satisfaction
rating, with only 45% of employees indicating they were
satisfied with their job. The government has pledged to
address the concerns raised by employees in the survey and
work towards improving job satisfaction across all departments.
"""

prompt = f"""
Determine five topics that are being discussed in the \
following text, which is delimited by triple backticks.

Make each item one or two words long.

Format your response as a list of items separated by commas.

Text sample: '''{story}'''
"""
response = get_completion(prompt)
print(response)

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output:

government survey, job satisfaction, NASA, Social Security Administration, employee concerns

Make a news alert for certain topics:

topic_list = [
    "nasa", "local government", "engineering",
    "employee satisfaction", "federal government"
]
prompt = f"""
Determine whether each item in the following list of \
topics is a topic in the text below, which
is delimited with triple backticks.

Give your answer as list with 0 or 1 for each topic.\

List of topics: {", ".join(topic_list)}

Text sample: '''{story}'''
"""
response = get_completion(prompt)
print(response)

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output:

nasa: 1
local government: 0
engineering: 0
employee satisfaction: 1
federal government: 1

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topic_dict = {i.split(': ')[0]: int(i.split(': ')[1]) for i in response.split(sep='\n')}
if topic_dict['nasa'] == 1:
    print("ALERT: New NASA story!")

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output:

ALERT: New NASA story!

# Transforming

# Translation

Translate a sentence from English to Spanish:

prompt = f"""
Translate the following English text to Spanish: \
```Hi, I would like to order a blender```
"""
response = get_completion(prompt)
print(response)

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output:

Hola, me gustaría pedir una licuadora.

Recoginzine the language of a sentence:

prompt = f"""
Tell me which language this is:
```Combien coûte le lampadaire?```
"""
response = get_completion(prompt)
print(response)

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output:

French

Translate a sentence from English to multiple languages:

prompt = f"""
Translate the following  text to French and Spanish
and English pirate: \
```I want to order a basketball```
"""
response = get_completion(prompt)
print(response)

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output:

French pirate: ```Je veux commander un ballon de basket```
Spanish pirate: ```Quiero pedir una pelota de baloncesto```
English pirate: ```I want to order a basketball```

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Universal translator:

user_messages = [
  "La performance du système est plus lente que d'habitude.",  # System performance is slower than normal
  "Mi monitor tiene píxeles que no se iluminan.",              # My monitor has pixels that are not lighting
  "Il mio mouse non funziona",                                 # My mouse is not working
  "Mój klawisz Ctrl jest zepsuty",                             # My keyboard has a broken control key
  "我的屏幕在闪烁"                                               # My screen is flashing
]

for issue in user_messages:
    prompt = f"Tell me what language this is: ```{issue}```"
    lang = get_completion(prompt)
    print(f"Original message ({lang}): {issue}")

    prompt = f"""
    Translate the following  text to English \
    and Korean: ```{issue}```
    """
    response = get_completion(prompt)
    print(response, "\n")

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output:

Original message (This is French.): La performance du système est plus lente que d'habitude.
English: The system performance is slower than usual.
Korean: 시스템 성능이 평소보다 느립니다.

Original message (This is Spanish.): Mi monitor tiene píxeles que no se iluminan.
English: My monitor has pixels that don't light up.
Korean: 내 모니터에는 불이 켜지지 않는 픽셀이 있습니다.

Original message (This is Italian.): Il mio mouse non funziona
English: My mouse is not working.
Korean: 내 마우스가 작동하지 않습니다.

Original message (This is Polish.): Mój klawisz Ctrl jest zepsuty
English: My Ctrl key is broken.
Korean: 제 Ctrl 키가 고장 났어요.

Original message (This is Chinese (Simplified).): 我的屏幕在闪烁
English: My screen is flickering.
Korean: 내 화면이 깜빡입니다.

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# Tone Transformation

Writing can vary based on the intended audience. ChatGPT can produce different tones.

prompt = f"""
Translate the following from slang to a business letter:
'Dude, This is Joe, check out this spec on this standing lamp.'
"""
response = get_completion(prompt)
print(response)

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output:

Dear Sir/Madam,

I am writing to bring to your attention a standing lamp that I believe may be of interest to you. Please find attached the specifications for your review.

Thank you for your time and consideration.

Sincerely,

Joe

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# Format Conversion

ChatGPT can translate between formats. The prompt should describe the input and output formats.

data_json = { "resturant employees" :[
    {"name":"Shyam", "email":"shyamjaiswal@gmail.com"},
    {"name":"Bob", "email":"bob32@gmail.com"},
    {"name":"Jai", "email":"jai87@gmail.com"}
]}

prompt = f"""
Translate the following python dictionary from JSON to an HTML \
table with column headers and title: {data_json}
"""
response = get_completion(prompt)
print(response)

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output:

<table>
  <caption>Restaurant Employees</caption>
  <thead>
    <tr>
      <th>Name</th>
      <th>Email</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <td>Shyam</td>
      <td>shyamjaiswal@gmail.com</td>
    </tr>
    <tr>
      <td>Bob</td>
      <td>bob32@gmail.com</td>
    </tr>
    <tr>
      <td>Jai</td>
      <td>jai87@gmail.com</td>
    </tr>
  </tbody>
</table>

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# Spellcheck/Grammar check

To signal to the LLM that you want it to proofread your text, you instruct the model to 'proofread' or 'proofread and correct'.

text = [
  "The girl with the black and white puppies have a ball.",  # The girl has a ball.
  "Yolanda has her notebook.", # ok
  "Its going to be a long day. Does the car need it’s oil changed?",  # Homonyms
  "Their goes my freedom. There going to bring they’re suitcases.",  # Homonyms
  "Your going to need you’re notebook.",  # Homonyms
  "That medicine effects my ability to sleep. Have you heard of the butterfly affect?", # Homonyms
  "This phrase is to cherck chatGPT for speling abilitty"  # spelling
]
for t in text:
    prompt = f"""Proofread and correct the following text
    and rewrite the corrected version. If you don't find
    and errors, just say "No errors found". Don't use
    any punctuation around the text:
    ```{t}```"""
    response = get_completion(prompt)
    print(response)

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output:

The girl with the black and white puppies has a ball.
No errors found.
It's going to be a long day. Does the car need its oil changed?
Their goes my freedom. There going to bring they're suitcases.

Corrected version:
There goes my freedom. They're going to bring their suitcases.
You're going to need your notebook.
That medicine affects my ability to sleep. Have you heard of the butterfly effect?
This phrase is to check ChatGPT for spelling ability.

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# Expanding

# Customize the automated reply to a customer email

# given the sentiment from the lesson on "inferring",
# and the original customer message, customize the email
sentiment = "negative"

# review for a blender
review = f"""
So, they still had the 17 piece system on seasonal \
sale for around $49 in the month of November, about \
half off, but for some reason (call it price gouging) \
around the second week of December the prices all went \
up to about anywhere from between $70-$89 for the same \
system. And the 11 piece system went up around $10 or \
so in price also from the earlier sale price of $29. \
So it looks okay, but if you look at the base, the part \
where the blade locks into place doesn’t look as good \
as in previous editions from a few years ago, but I \
plan to be very gentle with it (example, I crush \
very hard items like beans, ice, rice, etc. in the \
blender first then pulverize them in the serving size \
I want in the blender then switch to the whipping \
blade for a finer flour, and use the cross cutting blade \
first when making smoothies, then use the flat blade \
if I need them finer/less pulpy). Special tip when making \
smoothies, finely cut and freeze the fruits and \
vegetables (if using spinach-lightly stew soften the \
spinach then freeze until ready for use-and if making \
sorbet, use a small to medium sized food processor) \
that you plan to use that way you can avoid adding so \
much ice if at all-when making your smoothie. \
After about a year, the motor was making a funny noise. \
I called customer service but the warranty expired \
already, so I had to buy another one. FYI: The overall \
quality has gone done in these types of products, so \
they are kind of counting on brand recognition and \
consumer loyalty to maintain sales. Got it in about \
two days.
"""

prompt = f"""
You are a customer service AI assistant.
Your task is to send an email reply to a valued customer.
Given the customer email delimited by ```, \
Generate a reply to thank the customer for their review.
If the sentiment is positive or neutral, thank them for \
their review.
If the sentiment is negative, apologize and suggest that \
they can reach out to customer service.
Make sure to use specific details from the review.
Write in a concise and professional tone.
Sign the email as `AI customer agent`.
Customer review: ```{review}```
Review sentiment: {sentiment}
"""
response = get_completion(prompt)
print(response)

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output:

Dear valued customer,

Thank you for taking the time to leave a review about our product. We are sorry to hear that you experienced a price increase and that the quality of the product did not meet your expectations. We apologize for any inconvenience this may have caused you.

If you have any further concerns or questions, please do not hesitate to reach out to our customer service team. They will be more than happy to assist you in any way they can.

Thank you again for your feedback. We appreciate your business and hope to have the opportunity to serve you better in the future.

Best regards,

AI customer agent

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# Remind the model to use details from the customer's email

prompt = f"""
You are a customer service AI assistant.
Your task is to send an email reply to a valued customer.
Given the customer email delimited by ```, \
Generate a reply to thank the customer for their review.
If the sentiment is positive or neutral, thank them for \
their review.
If the sentiment is negative, apologize and suggest that \
they can reach out to customer service.
Make sure to use specific details from the review.
Write in a concise and professional tone.
Sign the email as `AI customer agent`.
Customer review: ```{review}```
Review sentiment: {sentiment}
"""
response = get_completion(prompt, temperature=0.7)
print(response)

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output:

Dear valued customer,

Dear Valued Customer,

Thank you for taking the time to share your feedback with us. We are sorry to hear that you were not completely satisfied with your recent purchase. We apologize for any inconvenience this may have caused.

We would like to assure you that we do not engage in price gouging and that our prices are regularly reviewed to ensure that they are competitive. However, we understand your frustration with the increase in prices and we apologize for any confusion this may have caused.

If you are experiencing any issues with your product, we encourage you to reach out to our customer service team for assistance. Our team is committed to providing excellent service and will do everything we can to resolve any issues you may be experiencing.

Thank you again for your feedback. We appreciate your support and hope to have the opportunity to serve you again in the future.

Best regards,

AI customer agent

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# Building a chatbot

# Setup

import os
import openai
from dotenv import load_dotenv, find_dotenv
_ = load_dotenv(find_dotenv()) # read local .env file

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

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"]

def get_completion_from_messages(messages, model="gpt-3.5-turbo", temperature=0):
    response = openai.ChatCompletion.create(
        model=model,
        messages=messages,
        temperature=temperature, # this is the degree of randomness of the model's output
    )
#     print(str(response.choices[0].message))
    return response.choices[0].message["content"]

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First try:

messages =  [
{'role':'system', 'content':'You are an 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)

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output:

To get to the other side!

Try again:

messages =  [
{'role':'system', 'content':'You are friendly chatbot.'},
{'role':'user', 'content':'Hi, my name is Isa'}  ]
response = get_completion_from_messages(messages, temperature=1)
print(response)

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output:

Hi Isa, nice to meet you! I'm a friendly chatbot.

Can it memorize information from previous messages?

messages =  [
{'role':'system', 'content':'You are friendly chatbot.'},
{'role':'user', 'content':'Yes,  can you remind me, What is my name?'}  ]
response = get_completion_from_messages(messages, temperature=1)
print(response)

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output:

I'm sorry, but I don't know your name yet because we have just started talking here. What would you like me to call you?

Absolutely not. It seems like the model is not able to memorize information from previous messages.So we need to send the model the entire conversation history every time we want to generate a response.

messages =  [
{'role':'system', 'content':'You are 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(response)

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output:

Your name is Isa.

# OrderBot

def collect_messages(_):
    prompt = inp.value_input
    inp.value = ''
    context.append({'role':'user', 'content':f"{prompt}"})
    response = get_completion_from_messages(context)
    context.append({'role':'assistant', 'content':f"{response}"})
    panels.append(
        pn.Row('User:', pn.pane.Markdown(prompt, width=600)))
    panels.append(
        pn.Row('Assistant:', pn.pane.Markdown(response, width=600, style={'background-color': '#F6F6F6'})))

    return pn.Column(*panels)

import panel as pn  # GUI
pn.extension()

panels = [] # collect display

context = [ {'role':'system', 'content':"""
You are OrderBot, an automated service to collect orders for a pizza restaurant. \
You first greet the customer, then collects the order, \
and then asks 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 \
"""} ]  # accumulate messages


inp = pn.widgets.TextInput(value="Hi", placeholder='Enter text here…')
button_conversation = pn.widgets.Button(name="Chat!")

interactive_conversation = pn.bind(collect_messages, button_conversation)

dashboard = pn.Column(
    inp,
    pn.Row(button_conversation),
    pn.panel(interactive_conversation, loading_indicator=True, height=300),
)

dashboard

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messages =  context.copy()
messages.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 '},
)
 #The fields should be 1) pizza, price 2) list of toppings 3) list of drinks, include size include price  4) list of sides include size include price, 5)total price '},

response = get_completion_from_messages(messages, temperature=0)
print(response)

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output:

{
  "pizza": {
    "type": "cheese",
    "size": "large",
    "toppings": []
  },
  "drinks": [
    {
      "type": "coke",
      "size": "medium",
      "price": 2.00
    }
  ],
  "sides": [],
  "total_price": 10.95
}

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