A long-context AI model has a context window designed to hold a large sequence of input and generated tokens. This can let the model consider an extended conversation, multiple documents, or a sizeable codebase without manually splitting every task into small independent prompts.
A larger advertised window does not guarantee that every part of the input will be used equally well. Attention cost, retrieval accuracy, positional encoding, memory use, and the tendency to overlook details in the middle of long inputs all affect practical performance. Efficient attention and careful evaluation are therefore important parts of long-context design.
ELI5
A long-context model can consider a much larger amount of text in one request than a model with a smaller context window. This can help it work across long conversations, many documents, or a sizeable codebase without splitting everything into isolated pieces.
For example, it might receive an entire report and several related notes before answering a question about their combined meaning. A large window does not guarantee perfect recall, though, because the model can still miss details or give less attention to information buried in the middle.
