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Avis du cours Prompt Engineering & AI with ChatGPT: Novice to Expert 2025 par Tarek Ahmed

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AI Secrets of Prompt Engineering, Automation, Coding, Marketing , Advanced Topics Covered

Format d'apprentissage Online Course

Sous-catégorie Prompt Engineering

Prix du cours $19.99 (list $9.99)

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AI Secrets of Prompt Engineering, Automation, Coding, Marketing , Advanced Topics Covered

Qui est Tarek Ahmed ?

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4,463 students

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English

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Que apprend-on dans le cours de Tarek Ahmed ?

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!

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