AI Glossary
What it means, why it matters to your business, and where it shows up in a real build decision.
In plain terms
The context window is the amount of text an AI model can consider at once, covering both your input and its response. It sets a hard limit on how much a model can read in a single pass. Larger windows cost more per use, which is why retrieval is often smarter than pasting everything in.
The context window is the AI's short-term memory. It holds everything in front of it, but once the conversation or document exceeds its capacity, earlier content starts to fall off the edge.
A company building a contract review tool discovers the model misses a key clause on page 40 because the full contract exceeds the context window. They switch to chunked retrieval to solve it.
Understanding context window limits is essential when designing any system that processes long documents or multi-turn conversations. It determines your retrieval and chunking architecture.
Pasting an entire knowledge base into the context window to avoid building a retrieval layer is an anti-pattern. It is slower, more expensive, and less accurate than selective retrieval.
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