If you spend any time with tools like ChatGPT, Claude, Gemini, or Grok, you’ve probably noticed the word context pop up everywhere.
And if you’re a nerd (I am), you may have wondered where the term came from. Surprisingly, it’s rooted in a Latin weaving term—literally about threads woven together. Linguists picked it up in the early 20th century, computer scientists ran with it in the 1950s, and if you’ve been around Windows long enough, you might remember when the right-click menu was actually called a “context menu.”
Why this matters: in generative AI, “context” is a real constraint, and it shows up in a few different ways.
In large language models, it generally breaks down into three uses:
- Input context
- Output context
- Model Context Protocol (MCP)
1) Input context
Input context is how much information a model can consider when producing an answer. This limit is measured in tokens, tiny pieces of text that average a few characters each.
Older models like GPT-3.5 would hit that limit quickly, leading them to summarize or drop details. Newer models have far larger windows—Gemini Pro can take in around a million tokens—but limits still matter, especially with big codebases.
2) Output context
Output context is simply how long the model’s response is allowed to be. A model might understand a massive amount of information but still only be able to output 8K tokens.
That’s why answers sometimes cut off mid-stream.
3) Model Context Protocol (MCP)
Finally, MCP is a newer mechanism for supplying additional structured information—something I’ll cover in a future post.
Understanding these flavors of context helps you get sharper, more reliable results, and keeps your AI companion from losing the thread (see what I did there?).