دانلود کتاب مهندسی سریع در 30 روز: راهنمای کامل مبتدیان

Why Learn Prompt Engineering? In today’s rapidly evolving technological landscape, artificial intelligence (AI) plays a crucial role in various industries. From automating mundane tasks to providing intelligent insights, AI has become an integral part of business operations and personal productivity. At the heart of AI-powered applications lies prompt engineering, a skill that enables users to interact effectively with large language models (LLMs). By crafting well-structured prompts, individuals can guide AI to generate relevant and precise outputs, making prompt engineering an essential competency for anyone working with AI models.
TABLE OF CONTENTS:
Day 1: Introduction to Prompt Engineering
• What is Prompt Engineering?
• The Role of Prompts in AI Models
• Real-World Applications of Prompt Engineering
Day 2: Understanding AI and NLP Basics
• How AI Models Process Language
• Tokenization, Embeddings, and Model Training
• Understanding GPT, LLaMA, and Other LLMs
Day 3: Crafting Your First Prompts
• Basic Prompt Structures
• Experimenting with Simple Queries
• Prompt Engineering Do’s and Don’ts
Day 4: Optimizing for Better Responses
• Using Clear and Specific Instructions
• Adjusting Tone, Style, and Format
• Leveraging Step-by-Step Thinking in Prompts
Day 5: Understanding Temperature and Model Parameters
• What Are Temperature and Top-k Sampling?
• How to Control Creativity and Randomness
• Fine-Tuning Model Outputs with Parameters
Day 6: Role-Based Prompting
• Defining AI’s Role in a Conversation
• Using Context to Improve Responses
• Creating Specialized AI Assistants with Roles
Day 7: Zero-Shot, One-Shot, and Few-Shot Learning
• What is Zero-Shot Prompting?
• Enhancing Accuracy with One-Shot and Few-Shot Prompts
• Best Practices for Few-Shot Learning
Day 8: Chain-of-Thought Prompting
• What is Chain-of-Thought (CoT) Reasoning?
• Using Step-by-Step Reasoning for Complex Queries
• Improving Logical Thinking with CoT
Day 9: Prompt Chaining for Multi-Step Tasks
• What is Prompt Chaining?
• Connecting Prompts for Long-Form Interactions
• Examples of Multi-Turn Prompt Strategies
Day 10: Using System Prompts for AI Assistants
• Understanding System Prompts in LLMs
• Setting Up Constraints and Personality Styles
• Designing Custom AI Personas
Day 11: Writing Effective Prompts for Coding Tasks
• Generating Code with AI Models
• Debugging and Explaining Code with Prompts
• Writing Documentation Using AI
Day 12: Data Analysis and Visualization with Prompts
• Summarizing Large Datasets Using AI
• Generating Charts and Graphs from Text Inputs
• Extracting Key Insights from Data
Day 13: Generating Content with AI
• Writing Articles, Blog Posts, and Reports
• Crafting Social Media Captions and Marketing Copy
• Overcoming AI Limitations in Content Generation
Day 14: Advanced Text Summarization
• Creating Concise Summaries from Long Texts
• Extracting Key Takeaways Using Prompts
• Different Styles of Summarization
Day 15: Prompt Engineering for Question-
Answering Systems
• How LLMs Handle Q&A Tasks
• Structuring Queries for Better Answers
• Reducing Hallucinations in AI Responses
Day 16: AI-Powered Creative Writing
• Generating Stories and Poetry
• Writing Scripts and Dialogues
• AI-Assisted Brainstorming Techniques
Day 17: Working with APIs and AI Tools
• Using OpenAI’s API for Prompt Engineering
• Automating Tasks with AI-Powered APIs
• Integrating AI into Your Applications
Day 18: Debugging AI Responses
• Identifying and Fixing Incorrect AI Outputs
• Handling Bias and Ethical Concerns
• Experimenting with Alternative Prompt Strategies
Day 19: Using AI for Learning and Research
• AI as a Research Assistant
• Fact-Checking and Verifying AI Outputs
• Using Prompts for Faster Learning
Day 20: Creating AI Chatbots with Prompts
• Designing Conversational AI Agents
• Handling User Inputs Effectively
• Building Personality-Driven AI Responses
Day 21: Automating Repetitive Tasks with AI
• Using Prompts for Automation
• AI-Powered Scheduling and Email Assistance
• AI-Generated Reports and Summaries
Day 22: Customizing AI for Business and Productivity
• AI for Marketing, Sales, and Customer Support
• Generating Business Strategies with AI
• Enhancing Productivity with AI Assistants
Day 23: Ethical Considerations in AI Prompting
• Understanding Bias in AI Responses
• Avoiding Harmful or Misinformed Outputs
• Responsible AI Usage and Safety Measures
Day 24: Fine-Tuning LLM Responses
• Customizing Outputs for Specific Use Cases
• Training AI with Fine-Tuned Prompts
• Improving AI Consistency in Responses
Day 25: AI for Personal Assistants and Virtual Agents
• Using AI for Daily Task Management
• AI for Personal Development and Coaching
• Building a Smart AI Companion
Day 26: Developing AI-Powered Applications
• Integrating AI into Web and Mobile Apps
• Using AI for Interactive Chatbots
• Exploring Future Trends in AI Development
Day 27: Evaluating and Measuring Prompt Performance
• How to Measure AI Response Quality
• Analyzing AI Model Output Consistency
• Refining Prompts for Optimal Performance
Day 28: Advanced Prompt Engineering Techniques
• Recursive Prompting and Self-Refinement
• Multi-Modal Prompting for Image and Audio AI
• Combining AI Models for Enhanced Performance
Day 29: Future of Prompt Engineering
• The Evolution of AI and Prompt Engineering
• Career Paths in AI-Powered Solutions
• Upcoming Advancements in LLM Technology
Day 30: Wrapping Up & Next Steps
• Reviewing Your Learning Progress
• Building Your Own AI Prompt Portfolio
• Continuing Your AI Journey
Appendix
• Prompt Engineering Cheat Sheet
• Best AI Prompting Tools & Resources
• Common Prompting Mistakes and Fixes
• AI Career and Freelancing Opportunities