After going through numerous articles on “Prompt Engineering” authored by masters of AI, founder’s & numerous 20 year old’s; I thought it would be pretty good experiment if i just ask popular LLM tools to help me write a book titled “Prompt Engineering in 2024”. Following is the comparative study & description of the results achieved. Passing on the craft in good faith that it will help you get better with using AI in your projects.
Prompt :
You are a Stanford Phd in CS with a strong inclination for teaching. You are tasked with writing a book on “Prompt engineering in 2024” for major occupations. What would be Index for this 100 page book.
This prompt was given to four LLM’s (AI tools). Individual results for each are shared below
| https://chatgpt.com/ | 1. Introduction to Prompt Engineering Definition and Importance Evolution of Prompt Engineering in 2024 2. Foundations of Prompt Engineering Natural Language Understanding Machine Learning Basics Deep Learning Fundamentals 3. Tools and Technologies GPT-3 and its Variants OpenAI Codex Other AI-Powered Prompt Generation Tools 4. Applications in Business Marketing and Sales Prompts Customer Service Automation Financial Analysis and Forecasting 5. Applications in Healthcare Medical Diagnosis Assistance Patient Communication and Education Drug Discovery Support 6. Applications in Education Personalized Learning Platforms Automated Grading and Feedback Content Creation Assistance 7. Ethical Considerations Bias and Fairness in Prompt Engineering Privacy Concerns and Data Security Responsible AI Use in Occupational Settings 8. Future Trends and Challenges Advances in Prompt Engineering Integration with Other Technologies (e.g., IoT, AR/VR) Addressing Societal and Regulatory Challenges 9. Case Studies Real-world Examples of Prompt Engineering Success Stories Lessons Learned and Best Practices 10. Conclusion Recap of Key Concepts The Future of Prompt Engineering in Major Occupations |
| https://gemini.google.com/ | A AI (Artificial Intelligence) API (Application Programming Interface) Automation Bias in prompts B Best Practices for Prompt Engineering Business Applications of Prompt Engineering C Chatbots: Designing prompts for effective interaction Content Creation with Prompts Cost-Benefit Analysis of Prompt Engineering D Data Integration with Prompts Debugging Prompts Designing User Interfaces for Prompt Engineering E Ethical Considerations in Prompt Engineering Evaluating Prompt Effectiveness F Future of Prompt Engineering G Generative AI and Prompts GPT-3 (and other large language models) H Human-in-the-Loop Prompt Engineering I Industry-Specific Prompt Engineering Techniques (Examples by profession) J Job Opportunities in Prompt Engineering K Knowledge Integration with Prompts L Legal Considerations in Prompt Engineering M Machine Learning Fundamentals for Prompt Engineering Marketing and Advertising with Prompts Measuring Success with Prompts N Natural Language Processing (NLP) O OpenAI API and Prompt Engineering P Persona-Driven Prompt Design Prompt Engineering Tools and Resources Prompt Injection Techniques Q Quality Assurance for Prompts R Regulatory Landscape for AI and Prompts S Safety and Security in Prompt Engineering Search Engine Optimization (SEO) with Prompts Social Media and Prompts T Testing and Refining Prompts Text summarization with Prompts The Prompt Engineering Workflow U User Experience (UX) Design for Prompt Engineering V Version Control for Prompts W Writing Effective Prompts X Explainable AI (XAI) and Prompt Transparency Y Yes/No Question Prompting Z Zero-Shot Learning with Prompts |
| https://www.perplexity.ai/ | 1. Introduction to Prompt Engineering 2. Evolution of AI and NLP 3. Importance of Prompt Engineering 4. Principles of Prompt Engineering 5. Types of Prompts in AI Models 6. Prompt Engineering Techniques 7. Applications of Prompt Engineering 8. Personalized AI and Prompt Engineering 9. Harnessing Creativity in Prompt Engineering 10. Generative AI and Prompt Engineering 11. Efficient AI and Prompt Engineering 12. Case Studies and Real-World Applications 13. Future Trends in Prompt Engineering 14. Conclusion and Future Directions |
| https://labs.perplexity.ai/ | Introduction (5 pages)Definition and importance of prompt engineering Brief overview of the field and its applications Part 1: Fundamentals of Prompt Engineering (20 pages) Chapter 1: Representation Learning and Graph Neural Networks (5 pages)Introduction to graph neural networks and their applications Representation learning for graph-structured data Chapter 2: Natural Language Processing with Deep Learning (5 pages)Introduction to NLP and its importance in AI Deep learning techniques for NLP, including PyTorch framework Chapter 3: Algorithmic Complexity and Data Structures (5 pages)Introduction to algorithmic complexity and Big-O notation Data structures and their applications in prompt engineering Chapter 4: Machine Learning with Graphs (5 pages)Introduction to machine learning with graphs Applications of graph-based machine learning in prompt engineering Part 2: Prompt Engineering for Specific Occupations (40 pages) Chapter 5: Prompt Engineering for Software Developers (10 pages)Introduction to prompt engineering for software development Techniques for generating code snippets and debugging prompts Chapter 6: Prompt Engineering for Data Scientists (10 pages)Introduction to prompt engineering for data science Techniques for generating data analysis and visualization prompts Chapter 7: Prompt Engineering for Healthcare Professionals (10 pages)Introduction to prompt engineering for healthcare Techniques for generating medical diagnosis and treatment prompts Chapter 8: Prompt Engineering for Educators (10 pages)Introduction to prompt engineering for education Techniques for generating educational content and assessment prompts Part 3: Advanced Topics in Prompt Engineering (20 pages) Chapter 9: Responsible AI and Prompt Engineering (5 pages)Introduction to responsible AI and its importance in prompt engineering Techniques for developing norms of AI responsibility Chapter 10: Generative AI and Prompt Engineering (5 pages)Introduction to generative AI and its applications in prompt engineering Techniques for generating prompts with generative AI models Chapter 11: Evaluating and Refining Prompts (5 pages)Introduction to evaluating and refining prompts Techniques for improving prompt quality and effectiveness Chapter 12: Future Directions in Prompt Engineering (5 pages)Introduction to future directions in prompt engineering Emerging trends and applications in prompt engineering Conclusion (5 pages)Summary of key takeaways from the book Future outlook and potential applications of prompt engineering Appendix (10 pages)Additional resources for further learning Glossary of key terms and concepts |
On a personal level, I’m mesmerised with the depth & variety of responses generated by each LLM.
Further Digging
Feeling somewhat satiated I prompted labs.perplexity.ai this command
Explain Chapter 8 (in 10 pages). Create original content. Make it rich with examples and context
In the era of large language models (LLMs), prompt engineering has emerged as a crucial skill for effective interaction with AI systems like ChatGPT. Crafting a well-designed prompt is no longer a simple task, but rather an art that requires careful consideration of various factors to elicit the desired responses from the model. This chapter will delve into the world of prompt engineering, exploring its principles, strategies, and best practices to help you master the art of prompting.
Understanding the Basics of Prompt Engineering Prompt engineering involves writing prompts that guide the language model to produce the desired output. It’s akin to software engineering, where the goal is to write source code that a computer can execute. A prompt is the text provided to the model before it generates output, and it’s essential to account for the model’s idiosyncrasies, limitations, and training data.
The Six Building Blocks of a Good Prompt A good prompt consists of six essential components: task, context, exemplars, persona, format, and tone. The task defines the action verb and end goal, while context provides background information to constrain the model’s possibilities. Exemplars are examples that improve the output quality, and persona refers to the tone and style of the response. Format and tone are self-explanatory, but crucial in shaping the output.
Strategies for Effective Prompting Several strategies can enhance the quality of prompts:
- Zero-shot prompting: Providing normal instructions without additional context.
- Few-shot prompting: Conditioning the model on a few examples to boost performance.
- Using delimiters: Adding special tokens or phrases to provide structure and instructions.
- Detailed, numbered steps: Breaking down complex prompts into smaller, specific steps.
- Iterative refinement: Refining prompts through experimentation and feedback.
- Role-playing: Incorporating system messages or role-playing techniques to enhance the interaction.
Advanced Prompt Engineering Techniques For more complex tasks, advanced techniques can be employed:
- Tree of thoughts: A hierarchical approach to organizing ideas and prompts.
- Reflexion: Using the model’s output as input to refine the prompt.
- Self-consistency: Ensuring the model’s responses are consistent with its previous outputs.
Best Practices for Prompt Engineering To become proficient in prompt engineering, it’s essential to:
- Keep prompts concise and clear.
- Use explicit instructions and specify the format of the expected answer.
- Incorporate system messages or role-playing techniques.
- Iteratively refine prompts through experimentation and feedback.
- Stay up-to-date with the latest developments and strategies in prompt engineering.
Conclusion Mastering prompt engineering is crucial for effective interaction with large language models like ChatGPT. By understanding the basics of prompt engineering, employing various strategies, and following best practices, you can craft prompts that elicit high-quality responses from the model. Remember, prompt engineering is an evolving field, and staying current with the latest developments will help you unlock the full potential of LLMs.
As you can observe, the result obtained is pretty in-contextual to original query.
Conclusion: With a bit of tweaking, editing & right foundational knowledge, a spirited person can literally write an original book on “any given topic” with these LLM’s (Large language learning models).
Also Read: Advanced Google ads mastery here.
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