This repository contains training materials, practical exercises, screenshots, and resources for the Prompt Engineering for Beginners Practical Learning Program.
Prompt Engineering is the practice of designing and optimizing instructions for Artificial Intelligence systems to achieve accurate, reliable, and high-quality outputs. This program introduces the fundamental concepts, techniques, and practical applications of prompt engineering using modern AI tools.
By the end of this program, participants will be able to:
- Understand the fundamentals of Prompt Engineering.
- Explain how Large Language Models (LLMs) process prompts.
- Apply various prompting techniques effectively.
- Design prompts for content generation, coding, and research tasks.
- Build simple AI-powered workflows.
- Evaluate and improve prompt performance.
- Explore career opportunities in AI and Prompt Engineering.
- Introduction to Prompt Engineering
- Foundations of Artificial Intelligence
- Understanding Prompt Engineering
- Large Language Models (LLMs)
- Core Prompt Engineering Concepts
- Designing Effective Prompts
- Prompting Techniques
- Advanced Prompting Methods
- Prompt Engineering for Content Creation
- Prompt Engineering for Software Development
- Prompt Engineering for Research and Analysis
- Introduction to Agentic AI
- Building AI Workflows
- Prompt Evaluation and Optimization
- Real-World Applications and Career Opportunities
- Final Project, Summary, and Discussion
| Day | Topic |
|---|---|
| Day 1 | Introduction to AI and Prompt Engineering |
| Day 2 | Large Language Models and Core Concepts |
| Day 3 | Designing Effective Prompts |
| Day 4 | Prompting Techniques |
| Day 5 | Advanced Prompting Methods |
| Day 6 | Content Creation, Coding, and Research |
| Day 7 | Agentic AI and AI Workflows |
| Day 8 | Final Project and Career Opportunities |
- Definition of Prompt Engineering
- Importance of Prompt Engineering
- Benefits of Effective Prompting
- Applications Across Industries
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- Artificial Intelligence Overview
- Machine Learning
- Deep Learning
- Generative AI
- AI Applications
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- Prompt Fundamentals
- Prompt Design Principles
- Prompt Structures
- Prompt Components
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- How LLMs Work
- Training Data
- Inference Process
- Limitations of LLMs
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- Prompts
- Tokens
- Context Windows
- Temperature
- Model Parameters
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- Role Prompting
- Context Engineering
- Constraints
- Output Formatting
- Best Practices
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- Zero-Shot Prompting
- One-Shot Prompting
- Few-Shot Prompting
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- Chain-of-Thought Prompting
- Self-Consistency
- Reflection Prompting
- Decomposition Techniques
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- Blog Generation
- Report Writing
- Social Media Content
- Marketing Content
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- Code Generation
- Debugging
- Documentation
- Testing
- Refactoring
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- Research Assistance
- Data Analysis
- Summarization
- Knowledge Extraction
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- AI Agents
- Agent Architectures
- Tool Usage
- Autonomous Systems
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- Workflow Design
- Task Automation
- Multi-Step Reasoning
- Process Optimization
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- Prompt Testing
- Performance Metrics
- Evaluation Methods
- Optimization Strategies
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- Education
- Healthcare
- Finance
- Legal Services
- Marketing
- Software Development
- Prompt Engineer
- AI Consultant
- AI Trainer
- LLM Developer
- AI Product Manager
- AI Automation Specialist
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Participants will:
- Define a real-world problem
- Design prompts
- Test outputs
- Improve performance
- Present findings
- Key concepts reviewed
- Lessons learned
- Future learning pathways
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Prompt-Engineering-Beginners/
│
├── README.md
├── slides/
├── exercises/
├── resources/
├── images/
│ ├── module1-example1.png
│ ├── module2-example1.png
│ ├── ...
│ └── module16-example1.png
│
└── project/
- ChatGPT
- Claude
- Gemini
- GitHub Copilot
- Cursor AI
- Microsoft Copilot
This project is provided for educational and learning purposes.
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