Language & LLMs

The models behind chat assistants, and everything built around them.

A large language model is a system trained to predict the next piece of text, scaled until that simple objective produces something startling. That description sounds reductive and is accurate — which is precisely why the field argues about what these systems understand.

This is the biggest field here, because it's where most practical work happens. It covers what goes in (tokens, context windows, system prompts), how you steer behaviour (prompt engineering, fine-tuning, RLHF), how you give models facts they don't have (RAG), and what goes wrong (hallucination).

The entries take positions. RAG is for knowledge, fine-tuning is for behaviour, and most teams reach for the second when they needed the first. Hallucination is over-diagnosed. A system prompt is not a security boundary.

Start with Large Language Model, then RAG — it's the one people get wrong most expensively.

12 concepts in this field

Token

The small piece of text an AI reads and writes — usually a chunk of a word, not a whole word.

Large Language Model (LLM)

An AI trained on enormous amounts of text to predict the next piece of writing — the technology behind chatbots like ChatGPT and Claude.

Retrieval-Augmented Generation (RAG)

Letting an AI answer from a specific set of documents by looking them up as it responds — instead of relying only on what it memorized.

Fine-tuning

Continuing a model's training on your own examples so its behavior changes — baked into the model, not supplied at answer time.

Prompt Engineering

The craft of writing instructions that get the best, most reliable output from an AI model.

Hallucination

When an AI produces something fluent and confident that is simply false — fluency is not the same as accuracy.

Context Window

The maximum amount of text an AI can consider at once — its short-term working memory, measured in tokens.

Temperature

A single setting that controls how random or predictable an AI's output is — low for focused, high for creative.

Chain-of-Thought

Getting a model to reason step by step before answering — which dramatically improves its performance on hard problems.

Quantization

Storing a model's numbers with less precision so it fits in less memory and runs faster — usually at a surprisingly small cost in quality.

RLHF (Reinforcement Learning from Human Feedback)

Training a model on human preferences rather than correct answers — the step that turned text predictors into assistants.

System Prompt

The standing instructions a model gets before the conversation starts — influential, invisible to users, and not a security boundary.