Episode 14 — 1.5 Explain AI Concepts: Generative AI, LLM, NLP, Deep Learning, RPA
This episode builds exam-ready clarity around common AI terms that appear in modern data conversations and can show up in Data+ DA0-002 prompts as context or as part of a tool selection discussion. You will define generative AI as systems that produce new content, and you will explain large language models, natural language processing, and deep learning as related but distinct ideas with different goals and behaviors. You will also define robotic process automation as rule-driven task automation that often complements, but does not replace, statistical analysis or machine learning approaches. The emphasis is on practical comprehension: what each term means, what it is typically used for, and what misunderstanding would lead you to choose the wrong approach in a scenario. You will also connect these terms to data considerations such as training data, prompt inputs, and the difference between generating text and making predictions.
You will work through scenarios that mirror how the exam frames AI in a data workflow, such as using NLP to categorize support tickets, using an LLM to draft summaries that still require validation, or using RPA to automate data collection steps that follow consistent rules. You will cover risk and troubleshooting considerations, including bias, hallucinations, privacy exposure, and the need to verify outputs with trusted data sources. You will also practice describing “safe use” boundaries in plain terms, such as limiting sensitive data in prompts and validating AI-generated insights before publishing them. The outcome is confident term recognition plus the ability to apply the right concept to the right problem. Produced by BareMetalCyber.com, where you’ll find more cyber audio courses, books, and information to strengthen your educational path. Also, if you want to stay up to date with the latest news, visit DailyCyber.News for a newsletter you can use, and a daily podcast you can commute with.
