Agentic prompt engineering

In this course, you’ll gain a foundational understanding of how to craft effective prompts that guide large language models (LLMs) to deliver accurate, relevant, and useful responses. You’ll start by understanding what prompt engineering is, why it matters, and how LLMs process input to generate output. From there, you'll dive into a variety of prompting techniques including zero-shot, few-shot, and chain-of-thought prompting, learning when to apply each depending on your task or use case. 

You'll also explore AI agent prompt engineering, where prompts are used not just to retrieve information, but to guide autonomous agents that make decisions and take actions based on structured input. You’ll learn about the essential components of a well-structured agent prompt, its classification, and how to ensure your prompts are functional and optimized.

Designed primarily for Automation Developers, this course takes 1 hour and 15 minutes to complete.

• Introduction to LLMs and prompt engineering 

• Key techniques for effective prompting 

• AI agent prompt engineering basics 

• System vs. user prompts 

• Prompt evaluation and optimization 

At the end of the course, you should be able to:  

• Define prompt engineering and its role in effective LLM interactions. 

• Apply core prompting techniques and adjust LLM parameters to guide and refine model output. 

• Describe AI agent prompt engineering and the components of a well-structured prompt. 

• Differentiate between system prompts and user prompts in agent design. 

• Explain how UiPath supports prompt optimization and the factors influencing prompt health scores.