"Large language model" has become one of the most used phrases in tech, but it's often repeated without much explanation. Here's a plain-English account of what an LLM actually is, what it learned, and what that means for how you can use one.
What it is
A large language model is a type of software trained to understand and generate text. It learned by processing enormous quantities of written language — books, articles, websites, code, and more — and finding the statistical patterns in how words and ideas relate to each other.
The "large" refers to scale: both the volume of training data (hundreds of billions of words) and the number of parameters inside the model (the numerical values it adjusts during training). Scale turned out to matter enormously. Models that crossed certain size thresholds started doing things smaller models couldn't — reasoning through problems, translating languages they hadn't been explicitly taught, writing coherent long-form text.
The "language" part is literal: these models operate on text in and text out, though many now also handle images, audio, and structured data.
How it learned
Training an LLM works roughly like this: the model is shown a vast amount of text, and it repeatedly tries to predict what comes next. When it's wrong, it adjusts. When it's right, the adjustment is small. Over billions of iterations, the model develops an extraordinarily rich internal map of language — grammar, facts, tone, reasoning patterns, and the relationships between concepts.
This is called pre-training, and it produces a general-purpose model. Most LLMs then go through additional training steps — including a technique called reinforcement learning from human feedback (RLHF) — to make them more helpful, accurate, and safe to use.
What LLMs are good at
LLMs are genuinely strong at tasks that involve generating, transforming, or interpreting language:
- Writing and editing: drafting emails, reports, summaries, marketing copy, documentation
- Answering questions: synthesising information from what they learned during training (with caveats — see below)
- Translation and reformatting: converting text between languages, styles, or structures
- Code: writing, explaining, and debugging code, because code is text with very clear patterns
- Reasoning: working through multi-step problems in plain language, from maths to logical puzzles
What LLMs are not good at
LLMs have well-known limitations worth being clear about:
They can be confidently wrong. Because they're predicting plausible text rather than looking things up, they sometimes generate plausible-sounding but incorrect information — a phenomenon called "hallucination."
Their knowledge has a cutoff. Training data ends at a certain date. Events, publications, or changes after that date aren't known to the model.
They have no access to private information. Unless given it explicitly (via a document or a tool), an LLM knows nothing about your files, your company, or your data.
They don't learn from conversations. Each conversation typically starts fresh. The model isn't building up memory about you over time by default.
Many of these limitations are addressed through techniques like retrieval-augmented generation (RAG), tool use, and memory systems layered on top of the base model.
The major models
Several organisations have released LLMs that have become widely known: OpenAI's GPT family, Anthropic's Claude, Google's Gemini, and Meta's Llama (which is open-weight, meaning anyone can download and run it). Each has different strengths in reasoning, speed, cost, and context length. Comparing them across real tasks is the most reliable way to know which suits a given job — and why platforms that let you access multiple models are increasingly common. See the askFinz models page for a current comparison.
askFinz gives you access to the leading LLMs in a single workspace — switch between them, compare outputs, and find the one that works best for your task. Get early access.