When working with LLMs, most developers focus on prompt quality. But there's another factor that...
When working with LLMs, most developers focus on prompt quality.
But there's another factor that often gets ignored:
token consumption.
Tokens directly impact:
cost
Learn how LLM tokenization works, why it drives cost and latency, and practical ways to reduce token usage in your AI apps with…
LLM costs accumulate in ways that are not always obvious. Tokens consumed by system prompts, repeated context windows, and…
Your token costs are growing faster than your usage. You've already optimized model selection on...
Most developers think token optimization means shorter prompts. In 2026, the biggest costs come from bloated chat history, unused…
How prompt caching actually works When an LLM processes your input, it doesn't just read...
Author(s): Garvit Agarwal Originally published on Towards AI. Optimizing LLM Token Costs in Production: A Practical Engineering…
