Building Agentic AI Systems: Prompt Engineering for Beginners using Hugging Face and Groq, KPR Institute Engineering and Technology, Autonomous Engineering Institution, Coimbatore, India

Title
Building Agentic AI Systems: Prompt Engineering for Beginners using Hugging Face and Groq

Hybrid Event
Building Agentic AI Systems: Prompt Engineering for Beginners using Hugging Face and Groq
Guest Lecture Dept. Level
DATE
Mar 24, 2026
TIME
09:30 AM to 12:00 PM
DEPARTMENT
CS
TOTAL PARTICIPATES
152
Building Agentic AI Systems: Prompt Engineering for Beginners using Hugging Face and Groq Building Agentic AI Systems: Prompt Engineering for Beginners using Hugging Face and Groq
Summary

The session on “Building Agentic AI Systems” conducted by Mr. C. B. Veerakumar, Engineering Analyst at Wipro Technologies, Coimbatore, provided a comprehensive introduction to the concept and growing importance of AI agents in modern technology. The session began with a detailed theoretical explanation of AI agents, describing them as intelligent systems capable of perceiving their environment, processing information, and taking actions to achieve specific goals. The speaker elaborated on key principles such as autonomy, reactivity, proactiveness, and learning capability, which enable agents to function effectively in dynamic environments. In addition, the working process of AI agents—starting from input collection, followed by processing and decision-making, and ending with output generation—was clearly explained. Various types of AI agents, including simple reflex, model-based, goal-based, utility-based, and learning agents, were also discussed with examples to help students understand their real-world applications.

The session further progressed into an interactive hands-on implementation, where around 150 students actively participated in building their own AI agents. Using platforms like Google Colab, students wrote and executed code while integrating tools such as Hugging Face for pretrained models and Groq for fast inference. The concept of prompt engineering was emphasized, demonstrating how carefully designed prompts can significantly influence the behavior and accuracy of AI systems. Students experimented with different inputs, observed outputs, and refined their agents accordingly, gaining practical insights into AI development. Overall, the session successfully combined theoretical knowledge with real-world application, enhancing students’ understanding, technical skills, and confidence in working with emerging AI technologies.


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