# Large Language Model

> Part of the AI Garage Jargons glossary: https://aigarage.in/jargons (all terms: https://aigarage.in/jargons.md)

**Category:** LLM
**Tags:** beginner, generation, transformer, language-model

A neural network trained on large language datasets to estimate token probabilities and generate or transform sequences from the context it receives.

**In plain terms:** It predicts a useful next piece of language, adds that piece to the context, and repeats the process until it has produced a response.

**Use cases:**
- Conversational assistants
- Summarization and structured extraction
- Drafting and code generation

**Why it matters:** An LLM can perform many language tasks through one general interface, but its output is generated from learned patterns and current context. Product reliability still depends on evidence, evaluation, validation, and the workflow around the model.

**Why now:** LLMs increasingly sit inside products that also retrieve documents, call tools, process long context, and enforce structured outputs. Builders therefore need to distinguish the language model from the larger application that supplies evidence and controls actions.

**AI Garage example:** An AI Garage build-workbook assistant could use an LLM to turn approved session evidence into a clear instructional section. Retrieval chooses the relevant evidence, application code checks the required structure, and the LLM performs the language transformation.

**Mental model:** An LLM is a context-conditioned token predictor used repeatedly. It converts the available context into probabilities for the next token, selects a token, appends it, and runs again. Training shapes those probabilities; the surrounding product supplies current evidence, tools, limits, and validation.

**Builder scenario: Draft a support answer without confusing fluency with truth**

- **Context:** A support assistant receives a customer question and an approved policy passage, then asks an LLM to produce a concise answer.
- **Failure:** The application treats the model's fluent response as a database lookup and displays a plausible exception that the supplied policy does not support.
- **Intervention:** Give the model only the relevant policy evidence, require a structured answer with supporting excerpts, and validate consequential claims before displaying them.
- **Outcome:** Supported answers include the policy evidence used, while unsupported requests produce an explicit uncertainty or review path instead of an invented rule.

**Common misconceptions:**
- **Claim:** Calling an LLM a next-token predictor means it behaves like simple phone autocomplete.
  **Correction:** Next-token prediction describes the generation mechanism. A large neural network can use extensive context and learned representations to perform many transformations, but that capability still does not guarantee factual or logically correct output.

**Visual model (sequence):** A response is built through repeated token prediction. Fluency comes from learned language patterns, while factual support must come from the evidence and checks around the model.

**Visual description:** An LLM receives tokenized context, estimates probabilities for the next token, appends a selected token, and repeats that cycle until it produces a response.

**Related concepts:** [Token](https://aigarage.in/jargons/token), [Context Window](https://aigarage.in/jargons/context-window), [Neural Network](https://aigarage.in/jargons/neural-network), [Hallucination (AI)](https://aigarage.in/jargons/ai-hallucination)

**Video explainer:** [Large Language Model: predict, append, repeat](https://nqqbubjesbyuwrdhqxnk.supabase.co/storage/v1/object/public/aigarage-public-media/jargons/llm/v2/llm.mp4) (50 seconds)

See how tokenized context becomes next-token probabilities, how repeated prediction builds a response, and why the surrounding application must verify consequential claims.

**Published:** 2026-10-11T21:02:02+05:30
**Captions:** [English](https://aigarage.in/jargons/llm/captions.vtt)
**Narration:** generated

**Transcript:** A customer asks a support assistant if they can get a refund.

A large language model writes the reply. It does not look the answer up.

It predicts the next small piece of text, called a token.

It reads the question and the refund policy, then scores which tokens could come next.

One token is picked and added to the text. Then it predicts again.

Repeat that many times, and a full reply appears.

That is like phone autocomplete, but far more capable, because it uses much more context.

Still, a fluent reply is not a verified one.

So the application around the model supplies current evidence, checks key claims, and controls any action.

The model writes the language. Your application decides what it is allowed to become.

**Last researched:** 2026-10-11

**Sources:**
- [Google for Developers - Introduction to Large Language Models](https://developers.google.com/machine-learning/crash-course/llm)
- [Google for Developers - What's a large language model?](https://developers.google.com/machine-learning/crash-course/llm/transformers)

**URL:** https://aigarage.in/jargons/llm