What Exactly Is a Large Language Model? A Simple Guide to AI, Transformers, and How They Work

Last week a friend asked why many people equate AI with large language models (LLMs). To answer this, the article first clarifies the hierarchy: artificial intelligence is the ultimate goal of creating machines that think like humans; machine learning is a concrete method for achieving AI; deep learning is a more powerful subset of machine learning that uses neural networks with many layers.

Within deep learning, a large language model is a specialized “super‑student” that focuses on language. It has read almost all books and articles on the Internet, so its massive parameter count enables not only fluent conversation but also reasoning, poetry, and code generation.

The article then defines a language model as a machine‑learning model that predicts or generates plausible text, such as autocomplete. It estimates the probability of a token or token sequence given a longer context, enabling tasks like text generation, translation, and question answering.

To be considered a “large” language model, a model typically needs at least 100 million parameters, and most modern LLMs have billions of parameters. Larger models generally exhibit stronger capabilities but require more computational resources and higher costs. The article illustrates this with the DeepSeek‑R1 family, which offers multiple versions of varying parameter sizes.

Training an LLM is likened to cultivating a “super intern” in three steps:

General education (pre‑training) : the model reads all books, webpages, and code in a massive “library,” learning grammar and knowledge but remaining a passive “bookworm.”

Job‑specific training (instruction fine‑tuning) : the model solves countless “question + answer” exercises, learning to follow commands, write code, and hold conversations, turning it into a capable assistant.

Performance evaluation (RLHF – Reinforcement Learning from Human Feedback) : human reviewers score the model’s responses (thumbs‑up or thumbs‑down), rewarding safe, polite, and helpful answers, ultimately shaping a “gold‑star employee.”

The breakthrough that enabled modern LLMs was the 2017 Transformer architecture, introduced in the seminal paper Attention Is All You Need . The Transformer replaces sequential RNN/LSTM processing with self‑attention, allowing the model to consider every token in the input simultaneously, assign attention scores, and compute weighted sums of all tokens. This yields high parallelism, constant‑length dependency paths, and better handling of long‑range relationships.

Self‑attention works by asking, for each token, “How important are all other tokens to me?” For example, in the sentence “悟鸣同学是一位布道师，他有一个朋友叫小明，他很喜欢 AI,” the model must decide whether the pronoun “他” refers to 悟鸣 or 小明, and attention scores guide that decision.

The article distinguishes “reasoning models” from non‑reasoning LLMs: reasoning models can perform multi‑step logical inference and provide explanations, whereas non‑reasoning models rely on pattern matching to generate answers quickly without explicit reasoning steps.

When calling an LLM via an API, most providers require a URL, an API key for authentication, the model name, a list of past messages (to maintain conversation context), and parameters such as max_tokens, temperature, top_p, stream, and stop. The article shows how Cherry Studio’s console (opened with Ctrl + Shift + I or Command + Option + I on macOS) displays the full request payload, confirming that the history array contains role‑annotated messages so the model knows what was said earlier.

Finally, the author invites readers to like, share, and follow the public account for more AI tool reviews and objective AI viewpoints, and to join an AI discussion group that includes students, teachers, and industry practitioners.