The Quick Guide to Prompt Engineering Generative AI Tips and Tricks for ChatGPT, Bard, Dall-E, and Midjourney
| Autor principal: | |
|---|---|
| Formato: | Libro electrónico |
| Idioma: | Inglés |
| Publicado: |
Newark :
John Wiley & Sons, Incorporated
2024.
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| Edición: | 1st ed |
| Ver en Biblioteca Universitat Ramon Llull: | https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009811315606719 |
Tabla de Contenidos:
- Cover
- Title Page
- Copyright Page
- Contents
- Preface
- Chapter 1 The Basics of Generative Artificial Intelligence
- Understanding AI, Machine Learning, and Deep Learning
- What Is AI
- What Is Machine Learning
- What Is Deep Learning
- What Is Generative AI
- What Is a Language Model?
- Applications of AI in Business
- Chapter 2 The Role of Prompts in Generative AI
- How Did Prompts Originate
- How Can You Provide Data Input to an AI System
- Making AI Accessible to Everyone
- How Prompts Guide the AI's Response
- What Is behind the Prompt
- How Do Generative AI Systems Understand Input and Provide Output
- What Goes behind the Scenes in a Generative AI System
- The Importance of Carefully Engineering Prompts
- Chapter 3 A Step-by-Step Guide to Creating Effective Prompts
- Various Platforms and Their Prompt Formats
- Recognizing Characters and Depth of Prompts
- Some Platforms May Have Prompts Character Limitation
- Prompts Need Depth and Context
- Differentiate between Implicit versus Explicit Prompts
- Testing Often and Iterate
- Understanding a Prompt Dictionary
- Consistency
- Optimization
- Training and Onboarding
- Flexibility
- Key Factors to Consider: Context, Clarity, and Conciseness
- Providing Context
- Clarity
- Conciseness
- Some Everyday Usage Examples
- Common Mistakes to Avoid When Crafting Prompts
- Chapter 4 Diving Deeper: Structure and Nuances of Prompts
- Understanding Different Components of a Prompt
- Introduction to the Anatomy of a Prompt
- The Role of Context in Shaping AI Responses
- Importance of Specificity and Clarity in Prompt Formulation
- Using Temperature and Max Tokens to Guide AI Responses
- Balancing Creativity and Control in Your Prompts
- The Art of Iterative Prompting
- Strategies for Iterative Improvement in Prompting.
- Common Pitfalls and How to Avoid Them
- Advanced Prompting Techniques
- Chapter 5 Prompt Engineering across Industry
- Chapter 6 Practical Guide to Prompt Engineering
- Step-by-Step Guide to Crafting Your First Prompt
- Step-by-Step Instruction Guide
- Step 1: Identify the Goal
- Step 2: Start Simple
- Step 3: Gauge the Initial Response
- Step 4: Refine and Contextualize
- Step 5: Test for Variability
- Step 6: Iterate and Adapt
- Step 7: Utilize Advanced Parameters (If Available)
- Step 8: Broaden Your Horizons
- Step 9: Document and Learn
- Step 10: Share and Collaborate
- Testing and Evaluating Your Prompts
- Understand the Importance
- Conduct Dry Runs
- Employ the A/B Testing Approach
- Gather Peer Feedback
- Use Quantitative Metrics
- Consider Diverse Scenarios
- Reiterate Based on Outcomes
- Understand Model Limitations
- Document Learnings
- Stay Updated
- Iterating and Refining Your Prompts
- Understand the Need for Iteration
- Analyze the AI's Output
- Enhance Specificity
- Adjust the Phrasing
- Provide Context
- Test across Multiple Scenarios
- Seek External Feedback
- Monitor Evolving Objectives
- Leverage Advanced Features
- Document and Review
- Prompt Engineering for Various Applications
- Customer Support Chatbots
- Research and Academic Analysis
- Creative Writing and Content Generation
- Financial Analysis
- Medical Diagnostics Assistance
- Language Translation
- Entertainment and Media
- Legal Applications
- E-commerce and Retail
- Urban Planning and Architecture
- Tips and Tricks for Advanced Prompt Engineering
- Multistep Prompts
- Utilize Contextual Tokens
- Layered Refinement
- Exploit the "Nudging" Technique
- Active Feedback Loops
- Prompt Templates
- Limit and Guide AI Exploration
- Explore AI's Metacognition
- Parallel Testing
- Stay Abreast with Evolving Models.
- Advanced Techniques: Machine Learning for Prompt Optimization
- Why ML?
- Supervised Learning for Prompt Refinement
- Reinforcement Learning (RL) in Action
- Hyperparameter Tuning for Prompts
- Clustering for Response Analysis
- Natural Language Processing (NLP) for Insight Extraction
- Feedback Loops with Active Learning
- Generative Models for Prompt Creation
- Transfer Learning for Rapid Adaptation
- Periodic Model Evaluation and Retraining
- Chapter 7 Ethical Considerations in Prompt Engineering
- Understanding Bias in AI and Prompts
- Data Imbalance
- Historical Biases
- Subtle and Unintentional Biases
- Feedback Loops
- Strategies for Reducing Bias in Your Prompts
- Comprehensive Training
- Diverse Input Review
- Use Neutral Language
- Fact-Based Design
- Dynamic Adaptability
- Use of Anti-bias Tools
- Thorough Testing
- Iterative Refinement
- Transparency and Openness
- Collaboration with Ethicists
- Community Engagement
- Setting Ethical Standards
- Documented Feedback Mechanism
- Ethical Guidelines for Prompt Design
- Prioritize Fairness and Avoid Discrimination
- Maintain Transparency
- Protect User Privacy
- Safeguard against Harmful Outputs
- Ensure Accountability
- Promote User Autonomy
- Ensure Cultural Sensitivity
- Avoid Reinforcing Stereotypes
- Practice Continual Learning and Adaptation
- Engage with a Diverse Group
- Implement Ethical Review Processes
- Educate and Empower Users
- Commit to Long-Term Responsibility
- Prompts and Privacy Considerations
- Collection of Personal Information
- Storage and Security
- Data Anonymization
- Data Sharing and Third-Party Access
- Children's Data
- Contextual Awareness
- Consent Revocation
- Legal and Regulatory Compliance
- Transparency and User Education
- Ethical Boundaries
- Audit Trails
- Feedback Loops.
- Regular Updates and Reviews
- Future Ethical Challenges in Prompt Engineering
- Sophistication of AI Responses
- Hyper-personalization
- Autonomy of AI Decisions
- Manipulative Prompts
- Inclusivity and Representation
- AI as a Social Actor
- Ethics of Emotional AI
- Privacy Erosion
- Economic and Employment Implications
- Deepfake and Reality Distortion
- Transparency and Accountability
- Regulation and Censorship
- Moral and Ethical Dilemmas
- Advanced Techniques: Automated Bias Detection
- Origins of Bias in AI
- Automated Bias Detection
- Benefits
- Techniques
- Challenges
- Ethical Considerations
- Applications in Prompt Engineering
- Case Studies: Search Engines
- The Road Ahead
- Chapter 8 Application-Specific Prompt Engineering
- Prompts for Creative Writing
- Bridging Human Imagination with AI
- Genre-Specific Prompts
- Inspiration and Idea Generation
- Character Development
- Dynamic Interaction
- Multimodal Applications
- Ethical Considerations
- Challenges and Limitations
- Future Potential
- Prompts for Business Applications
- Data Analysis and Insights
- Customer Support and Interaction
- Market Research and Consumer Insights
- Content Generation
- Financial Forecasting
- Personalization in Marketing
- Human Resources and Recruitment
- Ethical Implications
- Challenges
- Future Trajectories
- Prompts for Educational Uses
- Personalized Learning Paths
- Tutoring and Homework Assistance
- Language Learning
- Special Education Needs
- Interactive Simulations
- Assessment and Feedback
- Augmenting Classroom Discussions
- Historical and Cultural Contexts
- Challenges
- Ethical Implications
- Future Directions
- Prompts for Coding and Software Development
- Code Generation
- Debugging Assistance
- Code Optimization
- System Design and Architecture
- Integration and API Usage.
- Documentation and Commenting
- Code Review and Quality Assurance
- Predictive Troubleshooting
- Personalized Learning and Skill Development
- Ethical Implications and Best Practices
- Future Prospects
- Prompts for Entertainment and Gaming
- Storyline Generation
- Character Design and Evolution
- Dynamic Game Environments
- Music and Sound Effects
- Dialogue Generation
- Real-Time Player Feedback
- Predictive Gaming Trends
- Virtual Reality (VR) and Augmented Reality (AR) Experiences
- Ethical Gaming Practices
- Personalized Gaming Experiences
- Crowd Control and Online Moderation
- Gamification of Non-gaming Platforms
- Merchandising and Ancillary Content
- Advanced Techniques Personalized and Adaptive Prompts
- The Need for Personalization and Adaptability
- Personalized Prompts-Beyond Generic Queries
- Adaptive Prompts-Respondingto Dynamic Contexts
- Balancing Privacy with Personalization
- Feedback Loops for Adaptive Prompting
- Branching Prompts for Multistep Interactions
- Real-World Application: E-learning Platforms
- Personalized Health Care Recommendations
- Entertainment and Media Consumption
- E-commerceand Online Retail
- Challenges Ahead
- Continuous Learning and Evolution
- Chapter 9 Advanced Topics in Prompt Engineering
- A/B Testing of Prompts
- Understanding A/B Testing
- Why A/B Testing Is Crucial
- Setting the Right Metrics
- Crafting Variations
- Sample Size and Duration
- Analyzing Results
- Iterative Testing
- Potential Pitfalls
- Beyond Binary Testing
- Ethical Considerations
- Personalized and Adaptive Prompts
- The Rise of Personalization
- What Is a Personalized Prompt?
- The Need for Adaptive Prompts
- Data: The Backbone of Personalization
- Feedback Loops Are Crucial
- Dynamic Learning and Prompt Evolution
- Ethical Considerations
- Challenges of Over-Personalization.
- Multimodal Personalization.
