Better AI Answers Come From Cutting Context, Not Adding More

A software developer argues that reducing what goes into an LLM's context window typically improves output quality more than expanding it. Because language models attend to all provided tokens equally, irrelevant material competes with useful information and can anchor the model on wrong details. Content buried in the middle of a long context also receives less model attention than material placed at the start or end, meaning placement matters as much as inclusion. The author recommends a workflow of generous retrieval followed by aggressive trimming — removing full files, stale conversation history, and boilerplate — keeping only the task, the directly relevant facts, and the desired output format. This approach simultaneously improves answer accuracy and lowers token costs, making it one of the few optimizations that benefits both quality and budget.