4 Techniques to Optimize Your LLM Prompts for Cost, Latency, and Performance
Last Updated on December 2, 2025 by Editorial Team
Author(s): Eivind Kjosbakken
Originally published on Towards AI.
Learn how to greatly improve the performance of your LLM application
LLMs are capable of automating a significant number of tasks. Since the release of ChatGPT in 2022, we have seen more and more AI products on the market utilizing LLMs. However, there are still a lot of improvements that should be made in the way we utilize LLMs. Improving your prompt with an LLM prompt improver and utilizing cached tokens are, for example, two simple techniques you can utilize to vastly improve the performance of your LLM application.
The article discusses several techniques to optimize prompts for large language models (LLMs) to enhance performance, reduce cost, and minimize latency. Key strategies include utilizing cached tokens effectively, placing user questions at the end of prompts, and employing prompt optimizers for creating structured, clear prompts. Each technique aims to improve the quality of responses while facilitating ease of implementation, highlighting the importance of using efficient practices in the development of LLM applications.
Read the full blog for free on Medium.
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