Context Window

A context window is the amount of text an AI model can process at once, in a single conversation or request, which directly limits how much of a given page, or how many pages, the model can actually see and draw from when generating a grounded, cited answer.

Why it matters

A larger context window changes retrieval economics: a model with a small context window relies more heavily on chunking and passage retrieval to select relevant fragments, while a model with a much larger window can ingest fuller pages, shifting some incentive back toward comprehensive, well-structured long-form content.

Related terms

• Chunking

  Chunking is how AI systems split content into smaller passages, typically 200 to 500 tokens, before indexing and retrieving it, meaning a page is rarely evaluated as one continuous whole but as a set of independent segments competing separately for retrieval.

• Passage Retrieval

  Passage retrieval is the step where an AI system selects specific relevant text chunks, not entire pages, to build its answer, meaning a page can be retrieved for one passage while the rest of it never enters the AI system's consideration for that query at all.

• Large Language Model (LLM)

  A large language model (LLM) is an AI model trained on vast amounts of text to understand and generate human language. It's the underlying technology behind ChatGPT (OpenAI's GPT models), Claude (Anthropic), and Gemini (Google), and what every generative engine is built on top of.