About the Course
This course has three primary learning objectives. Firstly, learners will learn how to apply best practices in prompt engineering to improve communication and collaboration with colleagues in the field. Secondly, learners will learn how to utilize prompt engineering techniques to overcome limitations of large language models (LLMs) and improve their performance. Finally, learners will learn how to integrate LLMs with other applications to enhance business processes. By achieving these objectives, learners will be able to develop and apply advanced prompt engineering skills in real-world scenarios to improve communication, overcome LLM limitations, and enhance business processes.
Delivery Type
Self e-Learning
Modules
Module 1: Best Practices and Tips in Prompt Engineering (30 minutes)
Sub-module 1.1: Designing Effective Prompts (10 minutes)
Sub-module 1.2: Mitigating Biases and Model Limitations (10 minutes)
Sub-module 1.3: Model Configuration and Tuning, Tips and Tricks (10 minutes)
Module 2: Integrating LLMs with Other Applications (20 minutes)
Sub-module 2.1: Incorporating Content from External URLs (5 minutes)
Sub-module 2.2: Integrating with Google Sheets, Zapier, Slack, and Microsoft 365 (10 minutes)
Sub-module 2.3: Exploring Integration with Customer Relationship Management (CRM) Systems (5 minutes)
Module 3: Practical Applications of Prompt Engineering in Business (20 minutes)
Sub-module 3.1: Content Generation and Marketing (10 minutes)
Sub-module 3.2: Customer Support Automation (5 minutes)
Sub-module 3.3: Data Extraction and Summarization (5 minutes)
Module 4: Ethical Considerations and Responsible AI Use (10 minutes)
Sub-module 4.1: Addressing Biases in LLM Outputs (5 minutes)
Sub-module 4.2: Data Privacy and Security (5 minutes)
Module 5: Use Cases and Hands-on Exercises (10 minutes)
Sub-module 5.1: Generating Content: Emails, Marketing Copy, and Reports (3 minutes)
Sub-module 5.2: Analyzing Content: Summarizing and Simplifying Long Text (3 minutes)
Sub-module 5.3: Analyzing Data: Formatting Table Data for Input and Formulating Questions (4 minutes)