• views: 119
• date: 8 May 2025
• posted by: 0nelove

This course delves into the principles, strategies, and best practices of prompt engineering, a crucial aspect in shaping AI models' behavior and performance.
Digital Challenges and Problem-Solving
Enhance self-awareness and provide insights into patterns and triggers.
Help individuals stay present, observe their emotions without judgment, and reduce reactivity.
Requirements
No programming experience needed
Description
This course delves into prompt engineering principles, strategies, and best practices, a crucial aspect in shaping AI models' behaviour and performance. Understanding Prompt Engineering is a comprehensive course designed to equip learners with the knowledge and skills to effectively generate and utilize prompts in natural language processing (NLP) and machine learning (ML) applications. This course delves into prompt engineering principles, strategies, and best practices, a crucial aspect in shaping AI models' behaviour and performance.Module 1: Introduction to Prompt EngineeringLesson 1: Foundations of Prompt EngineeringOverview of prompt engineering and its significance in NLP and ML.Historical context and evolution of prompt-based approaches.Module 2: Types of Prompts and Their ApplicationsLesson 2: Closed-Ended PromptsUnderstanding and creating prompts for specific answers.Applications in question-answering systems.Lesson 3: Open-Ended PromptsCrafting prompts for creative responses.Applications in language generation models.Module 3: Strategies for Effective PromptingLesson 4: Probing PromptsDesigning prompts to reveal model biases.Ethical considerations in using probing prompts.Lesson 5: Adversarial PromptsCreating prompts to stress-test models.Enhancing robustness through adversarial prompting.Module 4: Fine-Tuning and Optimizing with PromptsLesson 6: Fine-Tuning Models with PromptsTechniques for incorporating prompts during model training.Balancing prompt influence and generalization.Lesson 7: Optimizing Prompt SelectionMethods for selecting optimal prompts for specific tasks.Customizing prompts based on model behavior.Module 5: Evaluation and Bias MitigationLesson 8: Evaluating Prompt PerformanceMetrics and methodologies for assessing model performance with prompts.Interpreting and analyzing results.Lesson 9: Bias Mitigation in Prompt EngineeringStrategies to identify and address biases introduced by prompts.Ensuring fairness and inclusivity in prompt-based models.Module 6: Real-World Applications and Case StudiesLesson 10: Case Studies in Prompt EngineeringExploration of successful implementations and challenges in real-world scenarios.Guest lectures from industry experts sharing their experiences.
Intended Learners for basics



   
   
   

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