Itinai.com amazingly inviting cute adorable round ai bot in t a10513ec 1018 489c 86ae bb0ce364e29c 2
Itinai.com amazingly inviting cute adorable round ai bot in t a10513ec 1018 489c 86ae bb0ce364e29c 2

How to Cut RAG Costs by 80% Using Prompt Compression

The text discusses techniques to improve the efficiency of large language models (LLMs) through prompt compression, focusing on methods such as AutoCompressors and LongLLMLingua. The goal is to reduce inference costs and enable faster and accurate responses. The article compares different compression methods and concludes that LongLLMLingua shows promise for prompt compression in applications like Retrieval-Augmented Generation.

 How to Cut RAG Costs by 80% Using Prompt Compression

Accelerating Inference With Prompt Compression

Introduction

The inference process can be costly and time-consuming when using large language models, especially for longer inputs. This hinders their deployment in real-life applications, limiting their potential impact.

The Problem

Fast models tend to score lower in performance, leading to challenges in deploying them for practical use. The cost of inference throughput can limit the widespread use of large language models by individuals or small organizations.

The Solution

One practical and cost-effective method to address this issue is prompt compression. By compressing the original prompt while retaining important information, this technique speeds up the language model’s processing of inputs, enabling faster and accurate answers.

AutoCompressors

AutoCompressors summarize long text into short vector representations called summary vectors, acting as soft prompts for the model. These summary vectors are optimized end-to-end to best suit the specific task. They can be used for applications like Retrieval-Augmented Generation (RAG) to improve efficiency.

Selective Context

This method removes predictable tokens from the data by assigning self-information values to each lexical unit. It then retains only those from the first percentile, effectively compressing the prompt while maintaining context and reducing input tokens.

LongLLMLingua

LongLLMLingua improves upon LLMLingua by incorporating the user’s question into the compression process. It uses a question-aware coarse-to-fine compression method, document reordering, compression ratios, and post-compression subsequence recovery to enhance the language model’s perception of key information.

Practical Application

Using Nicolas Cage’s Wikipedia page as an example, we demonstrated how prompt compression techniques can significantly reduce input tokens while retaining essential information for the language model to generate accurate responses.

Conclusion

Of the methods discussed, LongLLMLingua seems to be the most promising for prompt compression in RAG applications, offering a 6–7x reduction in input tokens while retaining key information needed for accurate responses.

For AI solutions that can redefine your way of work and evolve your company, connect with us at hello@itinai.com. Stay updated on leveraging AI by following us on Telegram t.me/itinainews or Twitter @itinaicom. Discover practical AI solutions at itinai.com/aisalesbot designed to automate customer engagement and manage interactions across all customer journey stages.

List of Useful Links:

Itinai.com office ai background high tech quantum computing 0002ba7c e3d6 4fd7 abd6 cfe4e5f08aeb 0

Vladimir Dyachkov, Ph.D
Editor-in-Chief itinai.com

I believe that AI is only as powerful as the human insight guiding it.

Unleash Your Creative Potential with AI Agents

Competitors are already using AI Agents

Business Problems We Solve

  • Automation of internal processes.
  • Optimizing AI costs without huge budgets.
  • Training staff, developing custom courses for business needs
  • Integrating AI into client work, automating first lines of contact

Large and Medium Businesses

Startups

Offline Business

100% of clients report increased productivity and reduced operati

AI news and solutions