How Large Language Models Actually Work
A plain-language explanation of how LLMs process and generate text, without oversimplifying or overclaiming.
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What this session would cover
Proposed outline — the mentor finalises the agenda once this topic is scheduled.
- 1Tokens: how text is broken into pieces
- 2Next-token prediction and why it produces fluent text
- 3Training and fine-tuning in plain language
- 4Context windows and why models forget earlier information
- 5Why LLMs hallucinate and what that means for users
- 6What LLMs are and are not good at
Who it's for
- • Anyone who uses AI tools and wants to understand them
- • Students and professionals without a maths background
You'd leave with
- A plain-language understanding of LLMs
- Better intuition for when to trust output
- Clearer expectations of AI tools
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