# What Is an LLM (Large Language Model)? | Gr8r | Gr8r Studio

> A large language model is an AI model trained on vast amounts of text to understand and generate language. How LLMs work, what they do well and their limits.

Source: https://gr8rstudio.com/glossary/large-language-model

Glossary · AI

# LLM (Large language model)

A large language model (LLM) is an AI model trained on very large amounts of text to predict and generate language. It can draft, summarise, classify, extract and reason over text, but it can also produce confident errors.

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In one line

A large language model (LLM) is an AI model trained on very large amounts of text to predict and generate language. It can draft, summarise, classify, extract and reason over text, but it can also produce confident errors.

Related

- [Prompt engineering](https://gr8rstudio.com/glossary/prompt-engineering)
- [Fine-tuning](https://gr8rstudio.com/glossary/fine-tuning)
- [AI hallucination](https://gr8rstudio.com/glossary/ai-hallucination)

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On this page

1. [Why LLMs matter](#s-0)
2. [How LLMs work](#s-1)
3. [Common LLM mistakes](#s-2)
4. [How Gr8r approaches LLMs](#s-3)

## Why LLMs matter

LLMs make it practical to automate work that involves reading and writing: emails, documents, forms, tickets and notes. Tasks that once needed custom rules can often be handled with a well-designed prompt and checks.

## How LLMs work

An LLM breaks text into tokens and predicts the most likely next token, one at a time, based on patterns learned in training. Instructions, examples and retrieved documents in the prompt steer what it produces.

Models differ in cost, speed, context length and skill, so the right model depends on the task.

## Common LLM mistakes

Treating output as fact without checking. Choosing the largest model by default when a smaller one is cheaper and good enough. Sending sensitive data to a provider without reviewing its terms.

## How Gr8r approaches LLMs

We pick models per task and test them against your real inputs. We treat model output as untrusted until the application checks it, and we design the interface for when the model is wrong.

Example

A claims team uses an LLM to pull the date, policy number and incident type from free-text emails into a form, with any field the model is unsure about highlighted for a person to confirm.

FAQ

## Questions

Ask anything else

Reading our answers…

- Which LLM should my product use?
It depends on the task, data sensitivity, cost and speed. We usually test two or three models on your own examples before choosing.
- Can I switch LLM providers later?
Yes, if the product is built with a layer between your code and the model. Prompts and evaluations may need adjusting for the new model.

## Related terms.

- [ 01 Prompt engineeringPrompt engineering is the practice of writing, structuring and testing the instructions, context and examples given to a language model so it produces useful, consistent output for a specific task. Explore ](https://gr8rstudio.com/glossary/prompt-engineering)
- [ 02 Fine-tuningFine-tuning is further training an existing AI model on a set of your own examples so it learns a specific style, format or task more reliably than prompting alone can achieve. Explore ](https://gr8rstudio.com/glossary/fine-tuning)
- [ 03 AI hallucinationAn AI hallucination is output from a language model that sounds confident but is false, made up or not supported by its sources, such as an invented citation, figure or policy. Explore ](https://gr8rstudio.com/glossary/ai-hallucination)

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