What Is a Large Language Model?

A large language model is an AI system trained on a massive corpus of text -- books, websites, code, academic papers, and more -- that learns statistical relationships between words and ideas. When you ask it a question, it does not query a database or retrieve stored facts. It generates a response one piece at a time, predicting what text is most likely to follow your prompt based on patterns it absorbed during training.

The term "large" refers to the scale of both the training data and the model parameters. Modern LLMs have hundreds of billions of parameters -- numerical weights in a neural network that encode learned relationships between concepts. GPT-4o, Claude 3.5, and Gemini 1.5 Pro are all examples of large language models.

The "language model" part describes the core task: modeling how human language works. The earliest language models were simple -- predict the next word in a sentence. Today's LLMs perform complex reasoning, write code, summarize documents, and answer questions across virtually every domain.

How LLMs Actually Work

Transformer Architecture

Nearly every modern LLM is built on the transformer architecture, introduced by Google researchers in 2017. The key innovation is a mechanism called self-attention, which allows the model to weigh the relevance of every word in a sentence against every other word simultaneously -- rather than reading text left-to-right like earlier models. This lets transformers understand context across long passages.

Token Prediction, Not Fact Retrieval

Here is the most important thing to understand about LLMs: they do not retrieve facts from a database. They predict tokens. A token is roughly a word fragment. The model generates text by repeatedly asking: given everything before this point, what token is most likely to come next?

This prediction is probabilistic. The model does not choose the single "correct" next word; it samples from a probability distribution. That is why the same prompt can produce different outputs on different runs, and why LLMs can produce text that is fluent and confident but factually wrong.

Training and Fine-Tuning

LLMs are trained in stages. Pre-training exposes the model to enormous amounts of text, teaching it language patterns and world knowledge. Fine-tuning then aligns the model toward helpfulness through Reinforcement Learning from Human Feedback (RLHF), where human raters score outputs and the model is trained to produce higher-rated responses.

The training data cutoff matters: an LLM trained on data through early 2024 has no knowledge of events after that date unless augmented with real-time retrieval tools -- as Perplexity and ChatGPT Browse mode are.

The Major LLMs Compared

Several distinct models power the AI tools most professionals use. Each has different strengths, training approaches, and areas where it underperforms.

No single model dominates all categories. This is the core argument for running queries through multiple LLMs simultaneously.

Why LLMs Hallucinate

Hallucination -- generating confident but false information -- is not a bug that will simply be patched. It is a structural consequence of how LLMs work. Because the model's goal is to predict plausible text (not verified truth), it will sometimes generate output that sounds authoritative but is wrong.

Common hallucination patterns include:

• Fabricated citations -- real-sounding journal articles or legal cases that do not exist
• Wrong statistics -- plausible-sounding numbers that are inaccurate
• Outdated information -- confident statements about things that changed after the training cutoff
• Confident gaps -- detailed answers about niche topics the model has limited training data on
• Sycophantic drift -- agreeing with a false premise in the user's question

Hallucination rates vary by model and query type. For more on this, see our guide to AI hallucination and how to detect it.

What Happens When You Run Multiple LLMs

Running multiple LLMs on the same query introduces a powerful signal: cross-model agreement. If eight independent models trained on different data using different architectures all return consistent answers, the probability that all of them hallucinated the same wrong answer in the same way is very low.

Conversely, when models disagree sharply, that disagreement is a warning signal. It tells you the query touches an area where models are uncertain, data is ambiguous, or training differences produce divergent outputs. That disagreement is itself valuable -- it tells you to dig deeper before acting on the answer.

This is the reasoning behind Search Umbrella: run every query through 8 models simultaneously, then measure consensus. No single model needs to be perfect; the system improves reliability through independent corroboration.

The Trust Score Explained

The Trust Score is Search Umbrella's cross-model consensus metric. When you submit a query, it runs through all 8 models. The Trust Score measures how many models agree on the core answer -- not just that they used similar words, but that their substantive claims are consistent.

The Trust Score does not guarantee accuracy -- it measures consensus, which correlates with accuracy but is not identical to it. For more detail, see the Trust Score methodology page. For guidance on multi-LLM tools, see our comparison of the best multi-LLM tools.

Frequently Asked Questions