In this episode, we look at the role of large language models (LLMs) as modern-day “artificial interns”, exploring how these systems are transforming the way we work. From content generation and coding help to customer service and knowledge retrieval, we examine how LLMs are used across augmented, transactional, and autonomous tasks.
We discuss a unique comparison: using LLMs for problem-solving in the same way developers use rubber duck debugging—talking through issues to arrive at clearer solutions. But while LLMs offer immense value through 24/7 availability, wide-ranging knowledge, and responsiveness, the episode also unpacks their limitations, including hallucinations, biases, and the risk of overreliance without proper human oversight.
We also compare LLMs to traditional software and human assistants, highlight current real-world applications, and speculate on how these tools may evolve—raising important questions about ethics, trust, and professional responsibility. Whether you’re a developer, writer, or manager, this episode offers insights into how to work with LLMs effectively—not just as tools, but as intelligent collaborators.
If you are interested in learning more then please subscribe to the podcast or head over to https://medium.com/@reefwing, where there is lots more content on AI, IoT, robotics, drones, and development. To support us in bringing you this material, you can buy me a coffee or just provide feedback. We love feedback!
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