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!
2 Likes
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