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.
Reviewed July 10, 2026Stable
When not to use it
- Anything requiring a guaranteed-correct answer. An LLM produces plausible text, not verified fact. For arithmetic, lookups, or policy decisions, use a calculator, a database, or a rule — and let the model call it.
- High-volume, narrow classification. A small fine-tuned classifier will be cheaper by orders of magnitude, faster, and more accurate at telling spam from not-spam.
- Where the input is confidential and you can't control where it goes. This is a procurement question, not a technical one, and it kills more projects than any benchmark.
Reach for something else instead
- Rules and regexes are unglamorous and still correct for structured, predictable input. If a regex solves it, a regex solves it.
- Smaller task-specific models beat general LLMs on narrow jobs at a fraction of the cost.
- Traditional ML (gradient boosting and friends) remains the right tool for tabular prediction, where LLMs are simply the wrong shape.
Read more on the blog
- How to read a model releaseEvery few weeks a lab announces a new frontier model and every headline says the same thing. Here's how to work out what actually changed, what the benchmark numbers mean, and which parts of the announcement are marketing.
- How AI models are trained: from raw text to a system that helpsA language model isn't programmed, it's grown, in stages, from a firehose of text into a system that answers helpfully. The full modern pipeline: pretraining, supervised fine-tuning, preference alignment, and the reasoning training that defines 2026, what each stage does, why none can be skipped, and how the recipe changed.
- How a sentence becomes an answer: an LLM end to endMost explanations of large language models cover one piece, attention, or tokens, or sampling, in isolation. This follows a single sentence all the way through the machine, from the moment you hit enter to the words that come back, so the pieces finally connect.
- What is a large language model (LLM)? Complete guideLarge language models are the systems behind ChatGPT, Claude, and Gemini, the technology that made AI feel like it could talk, write, and reason. Underneath, an LLM does one deceptively simple thing: predict the next token. This guide explains what an LLM is, how it is built from pretraining through alignment, how it runs, what it can do, and where it falls short.
Further reading
- Brown et al. (2020), Language Models are Few-Shot Learners — GPT-3, and the demonstration that scale alone changes what models can do.
- Kaplan et al. (2020), Scaling Laws for Neural Language Models, and Hoffmann et al. (2022), Training Compute-Optimal Large Language Models — the second corrected the first on how to spend a compute budget.
- Ouyang et al. (2022), Training language models to follow instructions with human feedback — InstructGPT, the step that turned a text predictor into something usable.
Primary sources, listed so you can check the claims on this page rather than take them on trust.
Where people go wrong
- Treating fluency as accuracy. The model's confidence is a property of its writing style, not its knowledge.
- Expecting reasoning to be reliable because it's usually reliable. The failure mode is silent and looks identical to success.
- Building on a single model with no evaluation harness. Without a way to measure quality, every prompt change is a guess and every upgrade is a gamble.
At a glance
FieldLanguage & LLMs
Core mechanismnext-token prediction
Built onthe transformer
Strengthlanguage & reasoning
Weaknessfacts & reliability
DifficultyBeginner → Intermediate
Often compared with
LLM vs. Traditional Search — generation from patterns vs. retrieval of existing pages.
Where this sits
23 concepts come first. Understanding it opens up 66 more.
10Levelsteps in
23Needs firstconcepts
66Opens up21% of 310
7Areascrosses fields
Computed from the prerequisite graph, not assigned. How this works