The Challenge: Every Token Costs

In the world of Large Language Models (LLMs), every token comes with a price tag. For organizations running thousands of prompts daily, these costs add up quickly. But what if we could reduce these costs without sacrificing the quality of interactions?

Real Results: Beyond Theory

Our experimental Semantic Prompt Compressor has shown promising results in real-world testing. Analyzing 135 diverse prompts, we achieved:

✅ 22.42% average compression ratio
📉 Reduction from 4,986 → 3,868 tokens
💡 1,118 tokens saved while maintaining meaning
🔒 \>95% preservation of named entities and technical terms

📌 Example 1

Original (33 tokens):

I've been considering the role of technology in mental health treatment.
How might virtual therapy and digital interventions evolve?
I'm interested in both current applications and future possibilities.

Compressed (12 tokens):

I've been considering role of technology in mental health treatment.

Compression ratio: 63.64%

📌 Example 2

Original (29 tokens):

All these apps keep asking for my location.
What are they actually doing with this information?
I'm curious about the balance between convenience and privacy.

Compressed (14 tokens):

apps keep asking for my location. What are they doing with information.

Compression ratio: 51.72%

💰 The Cost Impact

Let’s translate these results into real business scenarios:

Customer Support AI (100,000 queries/day):

Avg. 200 tokens per query
GPT-4 API cost: $0.03 / 1K tokens

Without compression:

20M tokens/day
$600 daily cost
$18,000 monthly cost

With 22.42% compression:

15.5M tokens/day
$465 daily cost
💸 Monthly savings: $4,050

🧠 How It Works: A Three-Layer Approach

1. Rules Layer

Instead of using a black-box ML model, we implemented a configurable rule system:

rule_groups:
  remove_fillers:
    enabled: true
    patterns:
      - pattern: "Could you explain"
        replacement: "explain"

  remove_greetings:
    enabled: true
    patterns:
      - pattern: "Hello, I was wondering"
        replacement: "I wonder"

2. spaCy NLP Layer

We leverage spaCy’s linguistic analysis for intelligent compression:

🧠 Named Entity Recognition to preserve key terms
🔗 Dependency parsing for sentence structure
📟 POS tagging to remove non-essential parts
🛠 Compound-word preservation for technical terms

3. Entity Preservation Layer

We ensure critical information is not lost:

🧪 Technical terms (e.g., "5G", "TCP/IP")
🧐 Named entities (companies, people, places)
📏 Numerical values and measurements
📚 Domain-specific vocabulary

🛠 Real-World Applications

🧑‍💼 Customer Support

Compress user queries while maintaining context
Preserve product-specific language
Reduce support costs, maintain quality

🛡 Content Moderation

Efficiently process user reports
Maintain critical context
Cost-effective scaling

📚 Technical Documentation

Compress API or doc queries
Preserve code snippets, terms
Cut costs without losing accuracy

✨ Beyond Simple Compression

What makes our approach unique:

Intelligent Preservation:
Maintains technical accuracy and key data
Configurable Rules:
Domain-adaptable, transparent, and editable
Transparent Processing:
Understandable and debuggable

⚠️ Current Limitations

Requires domain-specific tuning
Conservative in technical contexts
Manual rule editing still helpful
Entity preservation may be overly cautious

🔭 Future Development

ML-based adaptive compression
Domain-specific profiles
Real-time compression
LLM platform integrations
Custom vocabulary modules

✅ Conclusion

The results from our testing show that intelligent semantic prompt compression is not only possible — it's practical.

With a 22.42% average compression ratio and high semantic preservation, LLM-based systems can reduce API costs while maintaining the quality and clarity of interactions.

Whether you're building support bots, moderation tools, or technical assistants, prompt compression could be a key layer in your stack.

🧹 Project on GitHub:
👉 github.com/metawake/prompt_compressor

Open source, transparent, and built for experimentation.

Semantic Prompt Compression: Reducing LLM Costs While Preserving Meaning