# What are large language models (LLMs) and how do they work? — QANATONE

> LLMs are probabilistic models with billions of parameters, trained on internet text. A next-word prediction game yielding uncanny…

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# What are large language models (LLMs) and how do they work?

12 Sep 2026 6 min read Core Concept · LLM

LLMs are probabilistic models with billions of parameters, trained on internet text. A next-word prediction game yielding uncanny capabilities at scale.

## What do they do?

An LLM produces the most probable continuation for a given text (prompt). Simple on paper; but at sufficient scale, this yields near-human performance on coding, translation, summarization, reasoning, and even tool use.

## Architecture: Transformer

The Transformer (Vaswani et al., 2017) broke the sequential bottleneck of RNNs/LSTMs. **Self-attention** lets every token attend to every other token in parallel, enabling training across thousands of GPUs.

## Training stages

**Pre-training:** Masked language modeling on trillions of tokens — learns statistical structure of language, world knowledge, reasoning patterns. Cost: millions of GPU-hours.

**Fine-tuning / RLHF:** Reinforcement learning from human feedback aligns the model for instruction following, safety, helpfulness. This stage defines the model's "personality" and guardrails.

## Limits and risks

LLMs are **reasoning engines, not knowledge bases** . Hallucination, training-data bias, staleness, and cost are real. RAG and tool use partially mitigate these.

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