What is a large language model (LLM)?
Large language models help businesses understand and act on information faster. From summarizing data to improving responses, LLMs bring clarity, consistency, and speed to language-heavy tasks.
If AI is the technology that makes machines smart, then large language models (LLMs) are what let us humans make sense of all that smartness. From chatbots to coding assistants to predictive insights, LLMs turn scattered information into fluent, actionable language. They simplify complex requests, surface relevant knowledge on demand, and respond as if conversing with an actual person. But behind all that ease lies a system of staggering complexity.
They're also fast. Once trained, these models can scan huge volumes of text in seconds, generate responses in real time, and adapt to prompts with increasing fluency. And because they don't rely on hard-coded rules, they're uniquely suited to working with ambiguity, unstructured data, and changing inputs. Whether embedded into workflows or powering generative AI tools, LLMs are becoming essential for turning data into decisions; especially in industries where language, regulation, and scale collide.
They're also fast. Once trained, these models can scan huge volumes of text in seconds, generate responses in real time, and adapt to prompts with increasing fluency. And because they don't rely on hard-coded rules, they're uniquely suited to working with ambiguity, unstructured data, and changing inputs. Whether embedded into workflows or powering generative AI tools, LLMs are becoming essential for turning data into decisions; especially in industries where language, regulation, and scale collide.
LLM meaning and definition
A large language model (LLM) is a type of artificial intelligence that is trained to understand and generate human language. To accomplish this, it analyzes massive volumes of text to learn the statistical patterns, relationships, and structures that comprise them. Once trained, an LLM can fluently summarize documents, answer questions, write reports, and more.
They’re called “large” because they contain billions (or increasingly, trillions) of parameters and are trained on enormous datasets. Most use a transformer-based neural network architecture, which allows them to understand context across long passages of text, rather than one sentence or word at a time.
LLMs don’t “know” things in the human sense. They don’t reason or check facts. Instead, they make predictions based on everything they’ve learned from their training data.
Components and architecture of LLM
Each component plays a distinct role in how the model moves from input to response, and how it improves over time. These can be grouped into three general categories: Architecture (how the model is built), Data (what it learns from), and Processing (how it runs in real time).
Tokenizer (architecture)
Before an LLM can respond, it needs to break sentences down into pieces it can work with. The tokenizer splits text into manageable units such as words, sub-words, or characters. These are called tokens. For example, “omnichannel” might become two tokens (“omni” and “channel”), while an unfamiliar word might be broken into even more parts. This step helps the model “see” the structure of language.
Embedding layer (architecture)
Once tokens are created, they’re turned into numbers the model can understand. This layer assigns each token a position in a multi-dimensional space, based on meaning and context. Words like “shipment” and “delivery” end up near each other because they’re used in similar ways. This lets the model grasp nuance and relationships beyond literal definitions.
Note: The tokenizer and embedding layer work together – the first breaks text into parts, and the second gives those parts meaning that the model can process mathematically.
Note: The tokenizer and embedding layer work together – the first breaks text into parts, and the second gives those parts meaning that the model can process mathematically.
Transformer architecture (architecture)
This is the engine of the LLM. Transformers let the model analyze entire sequences of tokens at once, comparing each word to every other word in the sentence. It doesn’t read left to right like we do; instead, it weighs which words relate most closely to others, no matter where they appear. This ability to understand long-range dependencies is what enables LLMs to generate fluent and context-aware responses.
Model weights (architecture)
These are the model’s “memory.” Weights are internal settings that tell the model which patterns matter most. During training, millions (or billions) of these weights are adjusted until the model learns how strongly to link tokens, meanings, and context. When you enter a prompt, the model uses these weights to generate the most likely response.
Training data (data)
LLMs learn by reading huge volumes of text. This can be anything from technical documents to novels to conversation transcripts. They don’t memorize facts; they learn how ideas tend to appear together. That’s how they pick up tone, structure, mood, and even things like sarcasm or uncertainty.
Text generation process (processing)
When generating a response, the model looks at the full prompt and then one at a time, predicts the most likely next token in the sequence. This continues until a full response is formed. It’s not copying or reciting; it’s generating new language based on the patterns it learned during training.
Inference layer (processing)
This is the part that runs in real time when you interact with the model. It takes your input, processes it through all the steps above, and generates a response, often in under a second. This layer is also where performance and cost become critical factors, especially in production environments where speed and efficiency matter.