- Home
- Introduction
- Role of Prompts in AI Models
- What is Generative AI?
- NLP and ML Foundations
- Common NLP Tasks
- Optimizing Prompt-based Models
- Tuning and Optimization Techniques
- Pre-training and Transfer Learning
- Designing Effective Prompts
- Prompt Generation Strategies
- Monitoring Prompt Effectiveness
- Prompts for Specific Domains
- ChatGPT Prompts Examples
- ACT LIKE Prompt
- INCLUDE Prompt
- COLUMN Prompt
- FIND Prompt
- TRANSLATE Prompt
- DEFINE Prompt
- CONVERT Prompt
- CALCULATE Prompt
- GENERATING IDEAS Prompt
- CREATE A LIST Prompt
- DETERMINE CAUSE Prompt
- ASSESS IMPACT Prompt
- RECOMMEND SOLUTIONS Prompt
- EXPLAIN CONCEPT Prompt
- OUTLINE STEPS Prompt
- DESCRIBE BENEFITS Prompt
- EXPLAIN DRAWBACKS PROMPT
- SHORTEN Prompt
- DESIGN SCRIPT Prompt
- CREATIVE SURVEY Prompt
- ANALYZE WORKFLOW Prompt
- DESIGN ONBOARDING PROCESS Prompt
- DEVELOP TRAINING PROGRAM Prompt
- DESIGN FEEDBACK PROCESS Prompt
- DEVELOP RETENTION STRATEGY Prompt
- ANALYZE SEO Prompt
- DEVELOP SALES STRATEGY Prompt
- CREATE PROJECT PLAN Prompt
- ANALYZE CUSTOMER BEHAVIOR Prompt
- CREATE CONTENT STRATEGY Prompt
- CREATE EMAIL CAMPAIGN Prompt
- ChatGPT in the Workplace
- Prompts for Programmers
- HR Based Prompts
- Finance Based Prompts
- Marketing Based Prompts
- Customer Care Based Prompts
- Chain of Thought Prompts
- Ask Before Answer Prompts
- Fill-In-The-Blank Prompts
- Perspective Prompts
- Constructive Critic Prompts
- Comparative Prompts
- Reverse Prompts
- Social Media Prompts
- Advanced Prompt Engineering
- Advanced Prompts
- New Ideas and Copy Generation
- Ethical Considerations
- Do's and Don'ts
- Useful Libraries and Frameworks
- Case Studies and Examples
- Emerging Trends
- Prompt Engineering Useful Resources
- Quick Guide
- Useful Resources
- Discussion
Prompt Engineering - Case Studies and Examples
In this chapter, we will explore real-world case studies and examples of prompt engineering projects to showcase the practical application of prompt-based language models across various domains. These case studies will highlight how prompt engineering has been used to address specific challenges, improve user interactions, and optimize language model performance.
Customer Support Chatbots
Problem Statement − A company aims to improve its customer support system by deploying a chatbot that can efficiently handle customer queries and provide accurate responses.
Prompt Engineering Approach − Prompt engineers fine-tune a language model using the OpenAI GPT-3 API with a chat-based format. The model is trained on a dataset of historical customer queries and their corresponding responses. Custom prompts are designed to handle different types of queries, such as product inquiries, technical support, and order status updates.
Results − The chatbot successfully handles various customer queries, delivering contextually relevant responses. Through iterative improvements and user feedback analysis, prompt engineers enhance the model's accuracy and responsiveness. The chatbot significantly reduces customer response time and improves overall customer satisfaction.
Creative Writing Assistant
Problem Statement − A creative writing platform aims to assist writers by providing contextually appropriate suggestions for storylines, character development, and descriptive writing.
Prompt Engineering Approach − Prompt engineers leverage the Hugging Face Transformers library to fine-tune a language model on a dataset of creative writing samples. The model is designed to generate creative prompts for various writing styles and genres. Writers interact with the model using custom prompts to receive inspiration and ideas for their writing projects.
Results − The creative writing assistant proves to be a valuable tool for writers seeking inspiration. The model's diverse and imaginative responses aid writers in overcoming creative blocks and exploring new writing directions. Writers report an increase in productivity and creativity while using the creative writing assistant.
Multilingual Customer Service
Problem Statement − A global e-commerce company wants to enhance its customer service by providing multilingual support to users from diverse linguistic backgrounds.
Prompt Engineering Approach − Prompt engineers use the Sentence Transformers library to fine-tune a multilingual language model. The model is trained on a dataset containing customer queries in various languages. Custom prompts are designed to handle queries in multiple languages, and the model is capable of providing contextually appropriate responses in the user's preferred language.
Results − The multilingual customer service language model successfully caters to customers from different linguistic backgrounds. It accurately handles queries in multiple languages and provides responses that respect cultural nuances and preferences. Users appreciate the personalized support, leading to improved customer satisfaction and retention.
Conclusion
In this chapter, we explored case studies and examples of prompt engineering projects in different domains. From customer support chatbots to creative writing assistants and multilingual customer service, prompt engineering has demonstrated its versatility and effectiveness in a variety of applications. These case studies highlight the practical benefits of prompt engineering and illustrate its potential to optimize language models for diverse use-cases and domains.