How LLMs Work: A Non-Technical 2026 Explainer

You don't need the maths to think clearly about LLMs. You do need to understand the four moving parts below.

Table of contents

• Tokens
• Attention
• Training
• Alignment
• FAQ

Tokens

Text broken into small units. Every model reads and writes tokens, not characters or words.

Attention

The mechanism that lets a model weigh the importance of every token in context against every other.

Training

Predict the next token across trillions of examples. Then fine-tune on curated tasks.

Alignment

RLHF and constitutional methods shape behavior. This is why frontier models feel 'helpful' beyond raw prediction.

Why this matters

Understanding these four parts lets you predict what an LLM will and won't do — and choose tools accordingly.

FAQ

Will bigger models keep winning?

Not automatically. Data quality, post-training and inference tricks now matter more than raw scale.