AI Prompt Engineering For Beginners

AI Prompt Engineering Mastery : ========================= Course Duration : 2 Months (16 Classes) Class Duration : 50 Minute Online Class Class Timiings : 2 Days in Week evening (Sat, Sun). Discounted Fee for Saudi Students: $550 (SAR 2100) (Advance 50%, Remaining in 3 Installments) Payment Method : By Bank Transfer. Certificate will be provided on completion of course (sample certificate is attached). Thanks WhatsApp +923175484879 Email : [email protected] https://www.kftechsols.com/ ... Introduction • What is AI? • Importance of Prompt Engineering • Real-World Examples (Chatbots, Healthcare, Education, Marketing) • Course Objectives & Structure Chapter 1: Basics of AI and NLP Introduction to AI • Key Concepts • Understanding NLP • How AI Interprets Prompts • Exercises Chapter 2: Understanding Prompt Engineering What is Prompt Engineering? • Why It Matters • Types of AI Models • Key Elements of Effective Prompts • Common Challenges • Exercises Chapter 3: Crafting Effective Prompts Elements of a Good Prompt • Basic Structures (Direct, Question, Contextual, Fill-in-the-Blank) • Advanced Techniques • Examples • Refining Prompts • Exercises Chapter 4: Advanced Prompt Engineering Techniques Contextual & Iterative Prompting • Chain of Thought • Role-Based • Scenario-Based • Multi-Turn • Conditional • Prompt Chaining • Case Studies • Exercises Chapter 5: Practical Applications Business & Marketing • Customer Support • Education & Training • Content Creation • Creative Industries • Healthcare • Technology & Code • Legal & Compliance • Exercises Chapter 6: Common Pitfalls and Troubleshooting Ambiguity • Over-Complexity • Lack of Context • Bias • Over-Specification • Iterative Testing • Prompt Chaining • Case Studies • Exercises Chapter 7: Prompts for Creative Media Image Generation (Simple, Detailed, Stylistic) • Music Generation • Video Generation • Scriptwriting • Character & Plot Development • Case Studies • Exercises Chapter 8: Ethical Considerations Types of Bias (Data, Algorithmic, Selection, Confirmation) • Identifying & Reducing Bias • Cultural Assumptions • Inclusive Prompts • Ethical Scenarios • Exercises