Episode 22 — Large Language Models: What They Can and Can’t Do
This episode focuses on large language models (LLMs), which have moved from research labs into mainstream applications. LLMs are trained on massive datasets and billions of parameters, enabling them to generate fluent text, summarize documents, answer questions, and perform tasks across domains. For certification learners, the importance lies in understanding both capabilities and boundaries. LLMs excel at pattern recognition and producing convincing text, but they are not inherently grounded in truth and can generate incorrect or biased content. Recognizing these limitations is essential to answering exam questions about responsible use and system design.
Examples highlight common use cases such as chatbots, coding assistants, and automated content generation. Strengths include versatility and adaptability, while weaknesses include hallucinations, dependency on training data, and high computational cost. In exam contexts, learners may be asked which tasks LLMs are best suited for, and which require additional systems for accuracy and reliability. Best practices include combining LLMs with retrieval mechanisms, human review, or domain-specific fine-tuning. By balancing an appreciation of power with awareness of shortcomings, learners develop the exam-ready ability to analyze when LLMs are appropriate and how to mitigate their risks. Produced by BareMetalCyber.com, where you’ll find more cyber audio courses, books, and information to strengthen your certification path.
