Introduction to Prompt Engineering in 5G: Enable AI-Driven Networks

Generative AI is reshaping how telecom teams analyze networks, automate operations, and produce documentation. But there’s a catch—you need to know how to prompt it effectively. In this course, instructor Rahul Kaundal shows network engineers, operations professionals, and technical managers how to apply prompt engineering to real-world 5G challenges.

Learn the anatomy of an effective prompt, then move into advanced techniques like zero-shot, few-shot, and chain-of-thought prompting. Through hands-on labs in Google Colab using Gemini, you’ll get a chance to analyze sample 5G KPI reports, generate configuration scripts, and automate technical documentation. Along the way, Rahul covers key considerations for production use, including hallucination mitigation, data privacy, and iterative prompt refinement.

Learning objectives
Explain the foundational concepts of generative AI, large language models (LLMs), and transformers, and describe their convergence with 5G network technologies and operations.
Apply the core principles of prompt engineering—including role, context, task, and format—to effectively interact with AI chatbots and large language models for telecom use cases.
Differentiate between advanced prompting techniques such as zero-shot, few-shot, chain-of-thought (CoT), and prompt chaining, and evaluate their suitability for complex telecommunications problem-solving.
Design and execute prompts for analyzing 5G network KPIs, extracting insights from technical reports, and visualizing network performance data using cloud-based AI tools like Google Colab and Gemini.
Develop prompts that automate operational tasks in 5G environments, including configuration script generation, technical documentation, design query conversion, and network scenario simulation for training.
Assess the risks and limitations of generative AI in telecom—including hallucinations, security, and data privacy—and formulate best practices for iterative prompt refinement and the development of a personal prompt library.
Analyze the evolution from basic prompting toward AI agents and autonomous network operations (ANOs), and evaluate the future trajectory of generative AI in 5G network management.