☀️ AI Morning Minute: Context Stuffing

More information should mean better answers. With AI, past a certain point, it means worse ones and that surprises almost everybody.

Here’s a habit that feels smart and quietly wrecks a lot of AI setups. When an AI gives a shaky answer, the natural instinct is to give it more to work with. Paste in more documents, more history, more everything. Fill up that big context window since you’re paying for it anyway. That instinct has a name, context stuffing, and it tends to backfire.

What it means

Context stuffing is cramming as much information as possible into an AI’s context window (its working memory for a single request), on the theory that more context means a better answer. The logic sounds airtight. If the model can hold 200,000 words, why not hand it everything and let it sort out what matters?

Because it doesn’t always sort it out that well. When you bury the three sentences that actually matter under fifty pages of maybe-relevant filler, the model’s attention gets spread thin, and the important part can get lost in the pile.

Why it matters

• More context can make answers worse, not better. Researchers have a name for one version of this, “lost in the middle.” Models tend to pay closest attention to the very start and very end of what you give them, and stuff jammed in the middle gets skimmed. Feed it ten documents instead of the three good ones and it can start missing answers it used to nail.

• It quietly runs up your bill and slows everything down. Every extra word you stuff in is more tokens to process, so you pay more and wait longer for a reply. You’re spending real money to make the answer worse. That’s the part that stings once you notice it.

• It’s why “just use a bigger window” isn’t the fix people hoped for. When models got huge context windows, a lot of folks figured the whole problem was solved, just dump everything in. Turns out picking the right information still beats piling on all of it. Honestly this is the same lesson as chunking and context engineering, coming at you from a different door: what you feed the model matters more than how much.

Simple example

Imagine asking a friend for directions, but instead of just telling them where you’re headed, you hand them your entire life story, every trip you’ve ever taken, and forty pamphlets for towns you’re not even going to. Somewhere in that stack is your actual destination.

Now imagine you just say the one address. They answer in five seconds. Same friend, way better directions, because you gave them the thing that mattered instead of everything you had.