What are Large Language Models (LLMs)?

1. Machine learning: Teaching systems through data

If we talk at the most fundamental level, LLMs are powered by machine learning, a branch of artificial intelligence focused on learning patterns from data rather than relying on programmed rules.

Machine learning systems are exposed to enormous volumes of text and allowed to identify recurring patterns, detect relationships between symbols, and learn statistical regularities on their own.

2. Deep learning: Learning through probability

Deep learning is a more advanced form of machine learning designed to handle complex, high-dimensional data like language. These models learn by observing how patterns occur, estimating probabilities of what occurs next, and continuously refining predictions over time.  

Over time, this enables LLMs to generate better answers to the queries and become exceptionally good at predicting language that behaves like humans.

3. Neural networks: Core of LLMs

To support the contextual level of learning, LLMs are built using artificial neural networks. A neural network consists of an input layer where data enters, multiple hidden layers where patterns are learned, and an output layer where all predictions are generated. 

Each layer processes information and passes it forward when certain activation thresholds are met. As data moves through the networks, it thus transforms into meaningful representations. 

4. Transformer models: Learning the deep context

Like Google, you get the results instantly, and that too personalised as per your query. This is because of transformer architectures. Language is inherently contextual, and the meaning of a word often depends entirely on the words around it, the sentence structure, and the broader conversation. Transformer models address this through a mechanism called self-attention. 

Self-attention allows the model to examine all the words in a sentence at the same time, determine which words are most relevant to each other, and understand how meaning changes based on context. This capability enables LLMs to connect ideas across long passages, understand how sentences relate to each other, and recognize how different sentences influence each other.

That’s how a user gets a response to their query based on intent and contextual understanding. As transformer models are trained on massive datasets, they develop a strong ability to associate words, phrases, and concepts based on how they appear together across millions of examples. 

The combination of machine learning foundations, deep learning, neural network architectures, and transformer-based contextual understanding allows LLMs to function as powerful language intelligence systems rather than simple text predictors. This is the reason why LLMs are capable of adapting to new tasks and topics.

1. Data as the foundation

LLM training begins with large, diverse datasets drawn from various sources such as books, articles, technical documentation, code repositories, and public web content. The objective is to expose the model to many ways language is used in domain contexts and writing styles. Before training, data is cleaned and structured to reduce noise and improve learning efficiency.

2. Pretraining: How language works

The core learning phase is pretraining, where the model repeatedly predicts missing or next words in the text. Each incorrect prediction slightly adjusts the model’s internal parameters, thus gradually improving accuracy. With over billions of iterations, the model learns the language patterns but does not instruct or intend.

3. Scale and compute

Pretraining modern LLM requires enormous computational resources, often running thousands of GPUs running for weeks or months. This scale is not optional; many advanced capabilities emerge only when models reach sufficient size and data exposure.

4. Fine-tuning and alignment

Once the model is pretrained, it is refined through fine-tuning, where it is trained on smaller, curated datasets to improve instruction following, response clarity, and practical usefulness. This often includes human feedback where preferred responses are reinforced so the model outputs are most helpful.

5. Evaluation and ongoing improvement

Before deployment, LLMs are tested across benchmarks and real-world scenarios. Even after release, models are updated through fine-tuning and evaluation to improve performance, safety, and reliability.