What Is an LLM and How It Works, Explained Simply

An LLM (large language model) is a neural network trained on massive amounts of text that learns to predict the next piece of text given everything that came before it. That's the whole mechanical trick. Models like Claude, GPT, and Llama are all built on this same idea, scaled up with billions of parameters and trained on datasets covering books, code, websites, and conversations.

The "how it works" part breaks down into three layers: how the model represents language, how it predicts the next token, and how that raw prediction gets turned into something you can build a product with. Understanding all three matters if you're deciding how to integrate an LLM into an application rather than just chatting with one.

Tokens, Not Words

LLMs don't process text as words or sentences. They split input into tokens — chunks that might be a whole word, part of a word, or punctuation. "SubToAPI" might become three or four tokens; "the" is usually one. This matters practically because:

• 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 token-limited
• Longer prompts and longer conversations cost more tokens per request

When you send a request to a model, your text gets tokenized, fed through the network, and the output comes back as tokens that get decoded into text again.

Next-Token Prediction

Here's the core mechanism: given a sequence of tokens, the model outputs a probability distribution over what the next token is likely to be. It picks one (sometimes the most likely, sometimes a weighted random choice depending on a "temperature" setting), appends it to the sequence, and repeats. This is why streaming responses feel like the model is "typing" — it genuinely is generating one token at a time, in order, with no ability to go back and edit earlier tokens.

This also explains a few things people find confusing:

• Why models sometimes contradict themselves mid-answer. There's no global plan — each token is chosen based on what came before, with no lookahead.
• Why longer, more specific prompts produce better results. More context narrows the probability distribution toward what you actually want.
• Why the same prompt can give different answers. Temperature and sampling introduce controlled randomness.

Training: Two Distinct Phases

Getting from "predicts next token" to "useful assistant" happens in two stages:

1. Pretraining. The model is fed enormous amounts of raw text and learns general patterns of language, facts, reasoning, and code structure by trying to predict the next token, billions of times, adjusting internal weights when it's wrong.
2. Fine-tuning / alignment. The pretrained model gets further trained on curated examples of good responses (and often human feedback) to make it follow instructions, refuse harmful requests, and behave like an assistant rather than a raw text-completion engine.

This second phase is why modern LLMs respond to "write me a Python function" with actual working code instead of just continuing the sentence with more prose.

Context Windows and Memory

An LLM has no persistent memory between requests by default. Each API call is stateless — the model only "knows" what's in the current context window (the prompt plus conversation history you send it). If you want a multi-turn conversation, your application has to resend the prior messages each time. This is a common point of confusion for people new to building with LLMs: the model isn't remembering your last conversation, your code is just replaying it.

From Model to Product

Understanding the mechanics is one thing; using an LLM in production software is another. A raw model needs infrastructure around it:

• Authentication and API key management
• Request formatting (system prompts, message roles, tool definitions)
• Streaming output handling
• Usage tracking for billing or rate limiting
• Team access control if more than one person or service uses it

If you already have a Claude subscription and want to call it programmatically instead of building this infrastructure yourself, that's exactly what SubToAPI does — it turns your existing Claude access into a standard HTTPS API with application keys (sub_live_...), streaming, tool use, and usage metadata, without you having to manage separate billing or API contracts. You can see the request format in the docs and get a key running in a few minutes via the quickstart.

A basic request looks like this:

curl https://api.subtoapi.app/v1/messages \
  -H "Authorization: Bearer $SUBTOAPI_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "claude-3-5-sonnet",
    "max_tokens": 512,
    "messages": [
      {"role": "user", "content": "Explain the difference between tokens and words."}
    ]
  }'

The response streams back tokens as they're generated (see streaming for how to consume that in your app), which is the same underlying next-token process described above — just exposed through a clean API instead of a chat window.

Why This Matters for Builders

If you're building anything on top of an LLM — a support bot, a code assistant, a document summarizer — the token-by-token, stateless, context-window-bound nature of these models shapes almost every design decision: how you chunk documents, how you manage conversation history, how you estimate costs, and how you handle tool calls (see tools for how function-calling fits into this prediction loop). The model isn't magic; it's a very good pattern-matching engine over token sequences, and the engineering around it is what turns that into a usable product.

Questions

Is an LLM the same as AI? No. LLMs are one type of AI model, specifically for language. AI is the broader field that also includes image recognition, robotics, recommendation systems, and other approaches unrelated to text prediction.

Does an LLM understand what it's saying? Not in the human sense. It has no beliefs or awareness — it's producing statistically likely continuations of text based on patterns learned during training. The results often look like understanding because language itself encodes so much structure and reasoning.

Can I use an LLM without training my own model? Yes, almost everyone building products does this. You call an existing trained model through an API — sending a prompt and getting a response — rather than training a model from scratch, which requires massive compute and data most teams don't have.