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Intro to Large Language Models – a one

hour general-audience introduction to LLMs.

???? The Essence

The talk strips away the fluff around Large Language Models (LLMs) and gives a clear view of how they work, what they can and cannot do, and why they’re such a big deal right now.

 

✅ Key take-aways

LLMs are powered heavily by the transformer architecture and attention mechanisms, which let the model consider many parts of the input text simultaneously rather than in strict sequence.

Training is in two major phases: pre-training (learning vast amounts of text/data to build general knowledge) and fine-tuning / alignment (customising the model for a specific behaviour or domain). Reddit+1

These models are still fundamentally predictive machines: they predict the next word/token based on the input and learned patterns, rather than “understanding” in the human sense.

They can be extended with tools, external data retrieval, and interfaces (e.g., multimodal input, speech, image, reasoning chains) — opening them up to many new applications. Reddit+1

With possibility comes risk: issues such as prompt injection, data leakage, model hallucination, and other security/ethical concerns are real and increasingly important.

 

⚠️ What really matters for you to act on

Don’t treat LLMs like magic boxes — they do amazing stuff, but their outputs depend heavily on data quality, alignment, and how you interface with them.

The hill of value isn’t just building or deploying a big model — it’s about how you integrate it into real workflows, how you evaluate and control its outputs, and how you manage the risks.

Being aware of the tech’s boundaries is as important as appreciating its power. Knowing what it can’t reliably do helps you set the right expectation and design better systems around it.

Security and governance are not after-thoughts — they must be designed alongside capabilities.

For businesses or projects: ask not only “Can it generate text/improve workflow?” but also “How will we ensure correctness, maintain safety, measure impact, and iterate responsibly?”

In short: this lecture gives you the “map of the terrain” for LLMs — how they work, why they’re important, where they’re fragile — so you can navigate their use smartly rather than being dazzled by hype.