A context window in generative AI refers to the maximum number of tokens, or units of texts, a large language model (LLM) can process in a single forward pass. The context window defines the model’s effective memory span and determines the total amount of textual information it can condition on when generating outputs. This token budget typically encompasses:

• The users’ current prompt
• Prior conversation history
• Retrieved documents, as used in retrieval-augmented generation (RAG) systems 
• Internal system prompts or instructions

For example, as of this publishing, ChatGPT-4o offers a context window of 128,000 tokens, allowing the model to process extensive inputs, such as lengthy documents or comprehensive conversation threads, within a single interaction. When the combined input exceeds this limit, older tokens—typically from the beginning of the sequence—are truncated or omitted based on a predefined token selection strategy (e.g., least-recent-first or heuristic prioritization).

Why is the context window important in generative AI?

The context window size fundamentally impacts a model’s capacity to preserve semantic continuity, reference prior information, and generate contextually grounded responses. Larger context windows allow LLMs to:

• Maintain long-form dialogue coherence across many turns
• Ingest and reason over large documents or multiple information sources
• Reduce hallucinations by anchoring responses to relevant, in-context facts (especially in systems like RAG)

However, expanding the context window introduces trade-offs. Longer sequences increase the computational and memory complexity of the model—typically O(n²) for standard transformer-based architectures—thereby elevating latency and resource consumption. As such, optimizing the use of the context window is a critical factor in balancing model performance, inference cost, and practical deployment feasibility.