Prompt Engineering & AI with ChatGPT: Novice to Expert 2025 Review Summary
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Prompt Engineering & AI with ChatGPT: Novice to Expert 2025 · Program
About this program
Prompt Engineering & AI with ChatGPT: Novice to Expert 2025
Online Course
Learning format
Prompt Engineering
Subcategory
English
Course language
$19.99 (list $9.99)
Price
Price may change · updated within 1–2 weeks
Official program page
What you'll learn in Prompt Engineering & AI with ChatGPT: Novice to Expert 2025
- Introduction to Prompt Engineering , What is Prompt Engineering
- AI Key Terms Prompt(Instructions given to AI) Model(AI System Processing the prompt) LLM(AI Trained on vast Data) Token(Smallest Unit of AI Text Processing)
- How AI Models Work : Prediction , Learning & Limitations
- Types of AI Prompts : Instructional , Conversational & Role-Based
- Best Practices for Writing Effective Prompt : Be Specific , Give Context & Define Tone
- Common Mistakes in Prompt Engineering : Vague Prompts (too General)= Weak Answers Too Much (AI gets confused break it up) Ignoring (Refine and Retry Prompts)
- Testing and Refining Prompts : Test Versions (Small Tweaks Improve Answers) Add Details (AI Need Clear Instructions) Iterate(AI get Better with Refinement)
- Chain-of-Thought Prompting : Explain Steps (Force AI to explain step by step) Problem-Solving (Better for logical tasks) Useful(Good for Math and Reasoning)
- Zero-Shot (AI Guess Answer without Examples) versus Few-Shot Prompting (we give AI Examples for training , better and accurate Reponses)
- Role-Playing & Context Setting : Make AI an Expert (Doctor ,Teacher, Developer). Context Improves Responses. Useful for business, learning and Problem solving
- Multi-Step Prompts for Complex Tasks: Break big requests into smaller steps for better performance , AI is better with structured tasks
- Meta-Prompting for Recursive Self Improvements : iteratively refine AI-generated prompts , applications in autonomous AI optimization
- Neuro-symbolic Prompt-Integration (Symbolic Logic , Neural Networks , Hybrid Benefits)
- Adversarial Prompt Defense Mechanisms : Detection , Classification , Neutralization , Adaption Phases
- Hyperparameter Optimization for Prompts : Temperature Control , Top-k sampling , Top-p (Nucleus) Sampling, Repetition Penalties
- Cross-Modal Prompt Engineering : Text Modality , Visual Modality , Code Modality , Audio Modality
- Dynamic Context Window Management: Critical Info(Key context always retained) Recent Info(Near-term memory preserved) Background Info (Compressed for reference)
- Integrating AI into Business & Productivity: integrate into daily routine , Streamline emails , reports & Scheduling , save time and boost productivity
- Prompt Engineering for Coding & Automation : Code Generation(AI Writes & fix Code) Debugging(best with clear errors details) , (specify programming language)
- Key Use Cases for Prompt Engineering Optimization (Workflow across Industries, Real-World applications AI driven prompts , efficiency )
- AI Use Case: Research & Business Analysis : News Summarization , Academic Research , Business Reports
- AI Use Case: Customer Support & Legal (AI Chatbots , Email Writing , Legal and Compliance)
- AI Use Case: Human Resources & Software Development (HR & Recruitment , interview preparation (AI generate Questions)) , Coding & Debugging Writes and fix Code
- AI Use Case : E-Commerce & data Processing
- AI Use Case: Translation , Learning and Marketing
- AI Use Case: Security and Automation
- Ethical AI Prompting : Avoid Bias & Errors : Neutral Questions , Fast Check , Be Mindful
- The Future of AI & Prompt Engineering (Smarter AI , Valuable Skills , AI Assistance)
- How to Stay Ahead in AI & Prompt Engineering
- Final Thoughts & Next Steps in Prompt Engineering
- Final Challenge – Your AI Superpower!
What's Included in Prompt Engineering & AI with ChatGPT: Novice to Expert 2025
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