#4

Large Language Models explained briefly (YouTube) – a short explainer on what LLMs are “in plain English”.

(CLICK HERE TO WATCH)

???? Core Message

The video breaks down how large language models (LLMs) work at a high level — what happens inside, why they’re so powerful, and what that means for the future of AI. YTScribe

 

✅ What Really Matters

Prediction is the engine: LLMs are fundamentally mathematical functions that assign probabilities to “what word comes next” given a chunk of text. YTScribe+1

Scale changes everything: The “large” in LLM refers not just to data, but to parameters (weights) in the model — hundreds of billions of them — which allows the model to generalise across unseen inputs. YTScribe+1

Transformer + attention are enablers: These architectures let models process text in parallel and handle context effectively, rather than strictly sequential word-by-word. That’s a big break in capability. YTScribe+1

Training is enormous & complex: The model learns by ingesting massive volumes of text, then refining its predictions through mechanisms like backpropagation and reinforcement learning with human feedback (RLHF). YTScribe+1

Emergent behaviour & opacity: Because so many parameters are involved and so much training data is used, outcomes are sometimes remarkable — but the model’s behaviour is often hard to interpret or fully predict. YTScribe

 

???? Why This Is Strategic

If you’re using or evaluating LLMs, you need to understand they’re not “intelligent” in a human sense — they’re extremely advanced pattern predictors. That has implications for reliability, bias, and what they can truly do.

If you’re building solutions, your focus should be on context, data, architecture and deployment — more than just “using an LLM”. The architecture's capability and how you feed it data matter a lot.

For risk / governance, this video highlights: scale + complexity = less interpretability, more potential for unexpected results. So monitoring, evaluation and transparency are critical.

For strategy, if the model is doing “what word comes next based on patterns”, then you want to ensure the patterns you care about (your domain, your data) are properly included, aligned and governed.

 

???? Bottom Line

LLMs are a huge leap in capability, but the essence is still : predict-the-next-token at vast scale, with huge infrastructure behind it. They’re powerful — but not magic.
Understanding what happens inside matters because it tells you what they can’t do, where they may fail, and how you should use them to create value (rather than be fooled by hype).