Overview

Prompt compression reduces prompt size to save tokens and cost while preserving essential meaning. It combines techniques such as LLMLingua-style token-level compression, summary-based compression, and selective context inclusion to trim verbosity without sacrificing performance.

How This Skill Works

The skill applies multiple techniques to compress prompts: token-level LLMLingua, LLM-based summarization, selective context extraction of relevant sections, token pruning, and pre-retrieval document filtering. Configuration options like compression ratio targets and token budgets let you balance quality and cost, while evaluation metrics help you validate compressed prompts.

When to Use It

• You must operate under strict token budgets to reduce costs.
• You need faster responses with smaller prompts.
• Your data sources are long documents and reports.
• You want to test compression models or adjust quality thresholds.
• You want to filter documents before retrieval to reduce data.

Quick Start

1. Step 1: Assess the prompt to identify high-token sections.
2. Step 2: Choose a compression technique (LLMLingua, summary, selective context) and set targets.
3. Step 3: Apply compression, run tests with representative prompts, and measure cost impact.

Best Practices

• Monitor quality vs compression tradeoff
• Test with representative prompts
• Set appropriate compression ratios
• Validate compressed prompt quality
• Track cost savings

Example Use Cases

• Apply LLMLingua-style compression to a multi-turn customer-support prompt to cut tokens.
• Use summary-based compression to condense long incident reports into short briefs for LLMs.
• Implement selective context extraction to feed only relevant sections from large policy docs.
• Perform token pruning to remove low-importance tokens in recurring prompts.
• Enable document filtering pre-retrieval to reduce retrieved data and costs.

Frequently Asked Questions