Understanding Prompt Engineering
Last updated 3/2024
Duration: 43m | .MP4 1920x1080, 30 fps(r) | AAC, 44100 Hz, 2ch | 1.85 GB
Genre: eLearning | Language: English
Last updated 3/2024
Duration: 43m | .MP4 1920x1080, 30 fps(r) | AAC, 44100 Hz, 2ch | 1.85 GB
Genre: eLearning | Language: English
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.
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 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.
Who this course is for:
- Intended Learners for basics
More Info