A Large Language Model (LLM) is a deep learning model trained to understand, generate, and manipulate human language by predicting the next word (or token) in a sequence. It is typically built using the transformer architecture and contains hundreds of millions to hundreds of billions of parameters, enabling it to capture complex patterns in grammar, meaning, context, and even reasoning.

An LLM is trained on vast text datasets using self-supervised learning, allowing it to develop a general understanding of language without task-specific labels. Once trained, it can be used for a wide range of natural language processing tasks, including:

• Text generation,
• Translation,
• Summarization,
• Question answering,
• Code completion, and more.

Modern LLMs support zero-shot, few-shot, and in-context learning, meaning they can perform new tasks by simply being given examples or instructions in natural language — no retraining required. Examples of popular LLMs include GPT-4, PaLM, Claude, and LLaMA.

Despite their capabilities, LLMs come with challenges such as:

• Generating false or misleading information (hallucinations),
• Encoding social biases present in training data,
• High computational and environmental costs, and
• Security vulnerabilities like prompt injection.