Prompt Compression: Reduce LLM Costs and Latency Without Losing Quality

When you send a long prompt to a large language model, you’re not just asking for an answer—you’re paying for every word. prompt compression, the process of shortening input text while preserving its intent and meaning. Also known as input condensation, it’s becoming essential for anyone running LLMs at scale, whether you’re automating customer service, analyzing reports, or building AI agents. Most teams don’t realize how much they’re wasting on unnecessary tokens—sometimes over 70% of their prompt is fluff, repetition, or filler. Fix that, and you slash inference costs, reduce latency, and make your AI feel faster without changing a single model.

It’s not magic. LLM efficiency, how well a model uses compute and memory to deliver results. Also known as inference optimization, it’s the broader goal, and prompt compression is one of the easiest wins. You don’t need to retrain models or buy new hardware. You just need to clean up what you feed in. Tools like KV cache, a memory structure that stores past attention results to avoid recomputing them. Also known as key-value cache, it’s a core part of why LLMs slow down with long prompts get bloated because they keep repeating context. Compression cuts that noise. Teams using it report 30-50% lower token usage and 2x faster responses, especially in chatbots and agents that loop back on themselves. Even small fixes—removing redundant instructions, summarizing background info, or replacing paragraphs with bullet points—add up fast.

And it’s not just about saving money. Slower AI frustrates users. If your AI takes 5 seconds to answer because your prompt is 2,000 tokens long, people leave. But if you compress that same prompt to 800 tokens and get the same quality answer in 2 seconds? You keep them. That’s why companies like Unilever and Salesforce are testing automated prompt shorteners inside their workflows. They’re not replacing human judgment—they’re giving humans cleaner inputs to work with. You’ll find posts here that show you exactly how to do this: which techniques work for research summaries, which ones break down in agents, and which tools actually reduce hallucinations instead of causing them. Some methods are simple. Others need code. All of them are practical. What you’ll see below isn’t theory—it’s what teams are using right now to make their AI faster, cheaper, and more reliable.