Course Notes — ChatGPT Prompt Engineering for Developers

Link — https://www.deeplearning.ai/short-courses/chatgpt-prompt-engineering-for-developers/

Note: I highly recommend to have first hand experience from Andrew course only. This article is just having few main content/learnings from it for revision purpose.

Load OpenAI Key

Model used

Two types of LLMs

Principles of Prompting

1st Principle (Tactic — 01)

1st Principle (Tactic — 01 Example) — Use of Delimiter

Advantage of using delimiter — Avoid Prompt Injection (Conflicting instruction)

1st Principle (Tactic — 02)

1st Principle (Tactic — 02 Example) — Structured Output

1st Principle (Tactic — 03,04)

1st Principle (Tactic — 04 Example) — Few-shot prompt

2nd Principle (Tactic — 01)

Based on above principle, sample example I tried on https://chat.openai.com/

Example 01

Example 02

Mistakes happen when prompt is not upto the mark. Below is sample example —

correct by using structured prompt as above solution is indeed not correct.

Model Limitations

Summarization Examples

• Review

• Generate general feedback summary

• Generate feedback summary for price department

• Generate feedback summary for shipping department

Language Detection, Translation

Proof reading and correction

Difference before and after proof reading

Redline — python library used (https://pypi.org/project/redlines/)

Expanding — Brainstorm with LLM. If you have few words and want to write complete essay using same.

Temperature — Degree of randomness in expression

OrderBot — Automate the collection of user prompts and assistant responses to build a OrderBot. The OrderBot will take orders at a pizza restaurant. Below is the code copied from course.

### Step-01: Import Libraries
import os
import openai
from dotenv import load_dotenv, find_dotenv
import panel as pn  # GUI
pn.extension()

### Step-02: User defined functions

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)

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"]

### Step-03: Setup OpenAI

_ = load_dotenv(find_dotenv()) # read local .env file
openai.api_key  = os.getenv('OPENAI_API_KEY')

### Step-04: Setting context + Interactive bot
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
panels = [] # collect display 
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

Conclusion

LLMs are very powerful tools, please use them responsibly and create positive impact.

Please give a clap to this this article if it has helped you. I also welcome your feedback in the Comments section below.

Thanks !!