Podsumowywanie i rozwijanie tekstu

Prompt Engineering z OpenAI API

Fouad Trad

Machine Learning Engineer

Podsumowywanie tekstu

Skraca tekst do krótszej formy
Usprawnia procesy biznesowe
- Finanse -> podsumowuje obszerne raporty
- Marketing -> przekształca opinie klientów w wnioski
LLM potrafią streszczać teksty przy użyciu skutecznych promptów

Obraz pokazujący, jak podsumowanie zamienia stos książek w dokument zawierający streszczenie.

Nieskuteczny prompt

Określa jedynie tekst do podsumowania

text = "I recently purchased your XYZ Smart Watch and wanted to provide some feedback
based on my experience with the product. I must say that I'm impressed with the sleek design 
and build quality of the watch. It feels comfortable on the wrist and looks great with any 
outfit. The touchscreen is responsive and easy to navigate through the various features."


prompt = f"""Summarize the text delimited by triple backticks:
           ```{text}```"""
print(get_response(prompt))

The author purchased the XYZ Smart Watch and is impressed with its sleek design, build 
quality, and comfortable fit on the wrist. They find the touchscreen responsive and 
user-friendly for navigating the watch's features.

Ulepszanie promptów

Limity wyników
Struktura wyników
Zakres podsumowania

Obraz przedstawiający osobę wchodzącą po schodach jako symbol poprawy.

Skuteczny prompt: limity wyników

Określenie liczby zdań, słów lub znaków

text = "I recently purchased your XYZ Smart Watch and wanted to provide some feedback
based on my experience with the product. I must say that I'm impressed with the sleek design 
and build quality of the watch. It feels comfortable on the wrist and looks great with any 
outfit. The touchscreen is responsive and easy to navigate through the various features."


prompt = f"""Summarize the text delimited by triple backticks in one sentence:
           ```{text}```"""
print(get_response(prompt))

The customer is impressed with the sleek design, build quality, comfort, and responsiveness 
of the XYZ Smart Watch's touch screen.

Skuteczny prompt: struktura wyników

Określenie struktury wyników

text = "I recently purchased your XYZ Smart Watch and wanted to provide some feedback
based on my experience with the product. I must say that I'm impressed with the sleek design 
and build quality of the watch. It feels comfortable on the wrist and looks great with any 
outfit. The touchscreen is responsive and easy to navigate through the various features."


prompt = f"""Summarize the text delimited by triple backticks, in at most three bullet points.
           ```{text}```"""
print(get_response(prompt))

- The XYZ Smart Watch has a sleek and impressive design with  excellent build quality.
- It feels comfortable on the wrist and complements any outfit.
- The touch screen is responsive and user-friendly for easy navigation through the features.

Skuteczny prompt: zakres podsumowania

Polecenie modelowi skupienia się na konkretnych fragmentach tekstu

text = "I recently purchased your XYZ Smart Watch and wanted to provide some feedback
based on my experience with the product. I must say that I'm impressed with the sleek design 
and build quality of the watch. It feels comfortable on the wrist and looks great with any 
outfit. The touchscreen is responsive and easy to navigate through the various features."


prompt = f"""Summarize the review delimited by triple backticks, in three sentences, 
focusing on the key features and user experience:
           ```{text}```"""
print(get_response(prompt))

Skuteczny prompt: zakres podsumowania

The customer purchased the XYZ Smart Watch and was impressed with its sleek design 
and build quality. 
They found it comfortable to wear and versatile enough to match any outfit. 
The touch screen was responsive and user-friendly, making it easy to navigate 
through the watch's features.

Rozwijanie tekstu

Generuje tekst na podstawie pomysłów lub punktów
Zwiększa wydajność i produktywność
LLM potrafią rozwijać tekst przy użyciu odpowiednich promptów

Obraz ilustrujący rozwijanie tekstu jako generowanie pełnego tekstu na podstawie kilku pomysłów lub wymagań.

Prompty do rozwijania tekstu

Polecenie modelowi rozwinięcia wskazanego tekstu
Wskazanie aspektów, na których należy się skupić
Określenie wymagań dotyczących wyników (ton, długość, struktura, odbiorcy)

Ikona przedstawiająca ołówek piszący w notatniku.

Rozwijanie opisu usługi

service_description = """Service: Social XYZ
- Social Media Strategy Development
- Content Creation and Posting 
- Audience Engagement and Community Building
- Increased Brand Visibility
- Enhanced Customer Engagement
- Data-Driven Marketing Decisions"""

prompt = f"""Expand the description for the Social XYZ service delimited by triple 
backticks to provide an overview of its features and benefits, without bypassing 
the limit of two sentences. Use a professional tone.
```{service_description}```"""
print(get_response(prompt))

Rozwijanie opisu usługi

Social XYZ is a comprehensive social media service that offers strategic 
development, content creation, and posting to help businesses effectively engage 
with their target audience and build a strong online community. 

With a focus on increasing brand visibility and enhancing customer engagement, 
Social XYZ enables businesses to make data-driven marketing decisions for optimal 
results.

Czas na ćwiczenia!

Prompt Engineering z OpenAI API