Learning is better when it’s shared—and you’ve just explored the agentic twist on prompt engineering!
With this occasion, we invite you to share your “A-ha!” moments with us and fellow learners. If it’s easier for you to take some time and reflect on this lesson with some guidance, don’t hesitate to refer to one or more of the following questions:
What did I learn and did not expect?
What was difficult for me and how did I overcome it?
What is the most useful thing that I learned in this lesson?
Feel free to share with us and your peers whatever comes to your mind!
Agent memory and task chaining are game-changers.
The moment I saw how an agent can remember prior steps and build on them across tasks, it clicked: this is how AI moves from single-use tools to true digital co-workers.
Treating prompts as user experience design opened my eyes. Every prompt is a choice that affects how the agent reasons, behaves, and prioritizes goals.
Agentic Prompt engineering framework course not only touches the basic briefing about what are prompts and LLM Tokens, But also goes beyond with evaluation parameters for a better prompt. It teaches us various types of prompts, in depth explanation and example about System and User prompts as well as the the various parameters it will get evaluated on. It also throws light on how to choose your LLM.
Completed an AI Prompt Engineering course where I gained a solid understanding of prompt design techniques including zero-shot , one-shot , and few-shot prompting strategies.
I also learned best practices for structuring both system and user prompts to optimize performance and accuracy across various LLM applications
Just Now Completed the Agentic Prompt engineering course which is very important for every knows because right now AI is very advanced it helps to career growth and Upskill as Automation Developer. We can easily Understand like LLM,Embedding, Tokens, Traditional prompt vs AI Agentic prompt and many more .
I was actually waiting for this course to be available in academy. This would be very much helpful for the associates who are starting new with Agent (or agentic or AI Agents).
For your questions
What did I learn and did not expect?
Agent prompting (new term, though we know prompt in general) and traditional prompting. I did not expect it covered the Touching up on the LLM basics , parameters to choose. It would be great if you would have given some public sources to learn the current llm in market and its bench mark to link this parameters would be a good help.
What was difficult for me and how did I overcome it?
The difficult part is how we optimize the prompt and I realize the autopilot in agent builder would assist here. The good part is it uses the training data to suggest on the better prompts. Interesting.
What is the most useful thing that I learned in this lesson?
Agent Prompt and Traditional Prompt differences. Its a good one, though we know when such questions comes this actually helps to elaborate.
I was looking for courses and documentation to really help me getting better with prompts and we have the course just right there now!!
The course provides understanding on what prompt engineering is concepts like zero-shot prompt, chain of thought prompt really provide a deep understanding on to get best from LLMs.
The course also dove deeper in making us understand about System and User Prompt which helps in clearing defining roles of the agent and get the best out the agent.
Overall this the only course you need to level up your game and start building your agents.
Kudos to the team who designed the course and explaining concepts in clear, concise way to be understood by beginners as well.
In both life and language models, clarity is everything. whether you’re guiding a human or a machine. Completing my Prompt Engineering diploma taught me that the quality of input governs the quality of output. Large Language Models (LLMs) don’t “understand”.. they tokenize, embed meaning through high-dimensional vectors, and generate responses based on probabilistic token prediction. Set temperature parameter to 0, and you get predictable results; raise it, and creativity emerges… with this course I mastered techniques like zero-shot, few-shot, and chain-of-thought prompting. I learnt how to deal with the AI agent from asking questions to engineering direction, where prompts must be autonomous, multi-step reasoning. UiPath now quantify this with a Prompt Health Score via Agent Score, measuring how well your prompt enables autonomous, goal-driven actions.
I finished an AI Prompt Engineering course where I learned a lot about prompt design tactics, such as few-shot, one-shot, and zero-shot prompting.
Additionally, I discovered how to appropriately organize system and user instructions to maximize accuracy and effectiveness in a variety of LLM applications and difference between Traditional Prompt Engineering and AI Agent Prompt Engineering. It’s an excellent one, however as we all know, this really helps to clarify when such concerns arise.
This course taught me how to craft effective prompts that guide AI agents through complex, autonomous tasks. The course deepened my understanding of techniques like zero-shot, few-shot, and chain-of-thought prompting, and showed me how to structure both system and user prompts for clarity and precision. I also managed to explore the key differences between traditional prompts and agentic ones—highlighting how prompt design shapes an AI’s behavior, reasoning, and goal-orientation. This course sharpened my ability to think like a designer of interactions, not just a user of AI. I think AI Developer track is a good option to follow. Now I became more interested to complete more courses in this field. Thanks for UiPath for making this available.
Just completed the Agentic Prompt Engineering course! It provided clear insights into structuring System and User prompts, explained the logic behind agent workflows, and highlighted how to use arguments effectively. A must-learn for anyone working with AI automation and LLM-based agents.
The Agentic Prompt Engineering Framework course provided practical strategies to design goal-driven, context-aware prompts that enable more effective and autonomous AI behavior.
Completing the Agentic Prompt Engineering course from UiPath was a great learning experience. It gave me practical skills to design smarter prompts and build more intelligent automations using AI agents. I’m excited to apply what I’ve learned to create more adaptive and efficient workflows.
My A-ha! Moment from the Agentic Prompt Engineering Course
This course transformed how I see AI agents—from tools that respond to prompts into digital co-workers that reason, act, and adapt.
My biggest insight? System prompts are not just instructions—they’re the DNA of an agent’s identity and logic. Structuring them properly defines how the agent thinks, escalates, and completes complex tasks.
What surprised me?
The power of task chaining and arguments. Being able to carry reasoning across multiple steps made me realize this is more than just text generation—this is how you build autonomous workflows.
Most useful takeaway?
The clear distinction between traditional prompting vs agentic prompting. I now approach prompts as modular design components—thinking about role, logic, and outcome alignment. This mindset shift will absolutely improve my real-world AI automation projects.
Thanks to the UiPath team for making this accessible and practical. Looking forward to building agents that are not just reactive—but reliable collaborators.
I went into the course expecting a list of prompt tips — but the real game-changer was this: Think of prompts as instructions to an intelligent partner, not just a query to a machine.
Once I shifted from “How do I ask the right question?” to “How do I guide the AI step-by-step like a collaborator?”… my results changed completely.
My outputs became more accurate.
I got fewer “generic” answers.
And most importantly, I could replicate success across different use cases.
Now, instead of typing a single question and hoping for the best, I:
Define the role I want the AI to take.
Set clear context.
Break the task into stages.
The outcome? Faster, more consistent, and much more relevant results.
My biggest insight?
What surprised me?
Most useful takeaway?