What Is an LLM and How Does It Actually Work?

A large language model (LLM) is a type of AI system trained on huge amounts of text to predict the next word — or more precisely, the next token — in a sequence. Given a prompt, it generates a response one token at a time, each prediction based on everything that came before it. Models like Claude, GPT-4, and Llama are all LLMs, differing in size, training data, and architecture, but sharing the same core mechanism.

The "how it works" part comes down to three things: a neural network architecture called a transformer, a training process that teaches the model statistical patterns in language, and an inference process that turns a prompt into a generated response. Once you understand these three pieces, the rest of the "magic" around LLMs — chatbots, coding assistants, API products — starts to look like straightforward engineering built on top of a prediction engine.

Tokens, Not Words

LLMs don't read text the way humans do. Before anything reaches the model, text is broken into tokens — chunks that might be a whole word, part of a word, or a punctuation mark. "SubToAPI" might become two or three tokens; "the" is almost always one.

This matters practically:

• API pricing and rate limits are usually measured in tokens, not characters or words.
• Context windows (how much text a model can "see" at once) are also measured in tokens.
• Long conversations, large documents, or big code files consume the context window fast.

The Transformer Architecture

The transformer, introduced in 2017, is the architecture behind essentially every modern LLM. Its key innovation is self-attention: for each token being processed, the model calculates how relevant every other token in the input is to it, and weighs its prediction accordingly.

This lets the model handle things earlier approaches couldn't:

• Long-range dependencies — a pronoun on page 3 correctly referring to a name introduced on page 1.
• Context-sensitive meaning — the word "bank" is interpreted differently in "river bank" versus "bank account."
• Parallel processing — unlike older recurrent models, transformers process a whole sequence at once, which is why they train efficiently on modern GPUs.

Stack enough of these attention layers, feed them enough parameters (weights), and you get a model capable of writing code, summarizing documents, or holding a coherent conversation.

Training: How the Model Learns Language

Training happens in stages:

1. Pretraining — the model is fed massive amounts of text (web pages, books, code) and learns to predict the next token. This is where it absorbs grammar, facts, reasoning patterns, and coding syntax, purely as a byproduct of getting good at prediction.
2. Fine-tuning — the base model is adjusted on curated examples of the kind of behavior it should exhibit — following instructions, formatting answers, refusing unsafe requests.
3. Reinforcement learning from human feedback (RLHF) — human raters compare model outputs, and the model is nudged toward responses people actually prefer, improving helpfulness and reducing harmful or off-topic outputs.

None of this happens at request time. By the time you send a prompt, training is finished and the model's weights are frozen. What you're interacting with is a static set of parameters doing inference, not a system that's continuously learning from you.

Inference: What Happens When You Send a Prompt

Inference is the runtime process: your input is tokenized, converted into numerical representations, passed through the transformer's layers, and turned into a probability distribution over the entire vocabulary for what token comes next. The model samples from that distribution, appends the chosen token to the sequence, and repeats — one token at a time — until it produces a stop signal or hits a length limit.

This is also why LLM output can be streamed: since tokens are generated sequentially, they can be sent to the client as soon as they're produced, rather than waiting for the full response. That's the mechanism behind the typewriter effect you see in chat interfaces and the streaming responses available through most APIs, including SubToAPI's streaming endpoint.

Why This Matters for Building on LLMs

Understanding the mechanics has practical consequences for anyone building a product:

• Context window limits are real. Long conversations or documents can exceed the model's window, so applications need strategies like summarization or truncation.
• Determinism is limited. Because tokens are sampled from a probability distribution, the same prompt can produce different outputs across calls, unless the temperature is set very low.
• Tool use extends capability without changing the core model. LLMs can't browse the web or run code natively — they generate structured requests that an external system executes, then feed the result back in. SubToAPI's tool use support implements this pattern for building agents that call functions.
• API access turns a chat product into infrastructure. If your team already uses Claude in a browser, wrapping that access into an HTTPS API with keys, usage metadata, and streaming is what lets you actually build software on top of it rather than copy-pasting from a chat window. That's the specific gap SubToAPI fills — one dashboard, per-app API keys, and the same underlying model access, exposed the way a backend service expects.

Getting started with the API side is a matter of authentication and a single request:

curl https://api.subtoapi.app/v1/messages \
  -H "Authorization: Bearer $SUBTOAPI_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "claude-sonnet-4",
    "max_tokens": 1024,
    "messages": [
      {"role": "user", "content": "Explain how LLMs generate text, in two sentences."}
    ]
  }'

The quickstart guide walks through the full setup, and the Messages API reference covers request and response formats in detail.

Questions

Does an LLM actually understand what it's saying? No, not in the human sense. It has learned statistical patterns that correlate strongly with coherent, factual, and contextually appropriate language, which produces outputs that look like understanding without there being comprehension or beliefs behind them.

Why do LLMs sometimes make things up (hallucinate)? Because the model is generating the most statistically plausible next token, not looking up verified facts. If the training data was thin or ambiguous on a topic, the model will still produce a confident-sounding answer, correct or not.

Do LLMs get smarter from talking to users? No. The deployed model's weights are frozen at training time. It has no memory beyond the current conversation's context window, and nothing you type updates its underlying knowledge, though companies may use conversations to train future model versions separately.