loading
loading
Dumping a giant pile of documents into the prompt and hoping the model finds the answer, instead of retrieving the relevant bits.
With million-token windows it's tempting to skip retrieval and just paste everything in. Sometimes that works, but it's slow, expensive (you pay per token), and accuracy degrades as the model loses the signal in the noise — the needle-in-a-haystack problem. It's the lazy alternative to RAG: RAG retrieves the 5 chunks that matter, stuffing sends all 500 and prays. Use it as a quick prototype or when the corpus genuinely fits and is all relevant, but for anything large, retrieve first.
Plainly
Think of Context Stuffing as a simple recipe for doing the work better. Dumping a giant pile of documents into the prompt and hoping the model finds the answer, instead of retrieving the relevant bits.
In practice
Use it when you need a repeatable method instead of guessing from vibes. In practice, define the owner, input, output, and failure mode before you rely on it.