Understanding Prompt Engineering , Towards Excellence.
What you”ll learn:
- 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.
Course 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 Engineering
- Lesson 1: Foundations of Prompt Engineering
- Overview 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 Applications
- Lesson 2: Closed-Ended Prompts
- Understanding and creating prompts for specific answers.
- Applications in question-answering systems.
- Lesson 3: Open-Ended Prompts
- Crafting prompts for creative responses.
- Applications in language generation models.
Module 3: Strategies for Effective Prompting
- Lesson 4: Probing Prompts
- Designing prompts to reveal model biases.
- Ethical considerations in using probing prompts.
- Lesson 5: Adversarial Prompts
- Creating prompts to stress-test models.
- Enhancing robustness through adversarial prompting.
Module 4: Fine-Tuning and Optimizing with Prompts
- Lesson 6: Fine-Tuning Models with Prompts
- Techniques for incorporating prompts during model training.
- Balancing prompt influence and generalization.
- Lesson 7: Optimizing Prompt Selection
- Methods for selecting optimal prompts for specific tasks.
- Customizing prompts based on model behavior.
Module 5: Evaluation and Bias Mitigation
- Lesson 8: Evaluating Prompt Performance
- Metrics and methodologies for assessing model performance with prompts.
- Interpreting and analyzing results.
- Lesson 9: Bias Mitigation in Prompt Engineering
- Strategies to identify and address biases introduced by prompts.
- Ensuring fairness and inclusivity in prompt-based models.
Module 6: Real-World Applications and Case Studies
- Lesson 10: Case Studies in Prompt Engineering
- Exploration of successful implementations and challenges in real-world scenarios.
- Guest lectures from industry experts sharing their experiences.
