The context window is the maximum amount of text a language model can process at once, measured in tokens: instructions, conversation history, supplied documents and the generated answer all included.
Anything beyond that limit is ignored, or has to be summarized, split up or retrieved on demand. Windows have grown a lot: some models go beyond a million tokens, enough to hold several books or an entire codebase.
A large window does not solve everything. The more you load into the context, the more each call costs and the longer it takes, and models often make poorer use of information placed in the middle of a long text (the lost in the middle effect). Hence the value of RAG and careful context management: give the model only what is useful.
The topic is central for AI agents, which quickly pile up tool results and must sort out what they keep in memory.
Why it matters when hiring
A serious AI Engineer can explain how they decide what goes into the context, and at what cost. A good interview question: “Your document exceeds the window, what do you do?”. An answer limited to “we use a bigger model” shows surface knowledge. Real depth means talking about chunking, targeted retrieval, summarization and measuring quality.