Starving yourself is unproductive, but what happens when you starve your LLMs…of context?
Last Updated on January 3, 2026 by Editorial Team
Author(s): Surya Maddula
Originally published on Towards AI.
Starving your LLMs might be the key to contextual prompt reduction.
LLMs have remarkable capabilities for nlp tasks, but when deploying them, there’s always been a few challenges because of two main reasons: computational costs and memory constraints. And this is especially true when processing lengthy prompts.
The article discusses various strategies and techniques for reducing prompt sizes when working with large language models (LLMs) without sacrificing essential context or information. It introduces hard and soft prompt compression methods, highlights their benefits, and explains their applications across different scenarios, including retrieval-augmented generation, mathematical reasoning, and coding tasks. The piece concludes by emphasizing the necessity of understanding one’s specific requirements for selecting appropriate compression methods while acknowledging the trade-offs involved in achieving efficiency.
Read the full blog for free on Medium.
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