Reduce GPT Costs with Prompt Compression

Prompt compression is a technique that reduces the length of inputs given to large language models (LLMs). It aims to maintain output quality while using fewer tokens. This is important because LLM APIs (such as OpenAI, Anthropic, etc.) charge based on the number of tokens processed.

LLMs break down text into tokens, which are chunks of characters. Each token has a cost associated with it. Longer prompts use more tokens, leading to higher costs. Prompt compression helps you stay within token limits and reduce processing time.

Main Compression Techniques

There are three primary methods for compressing prompts:

1. Knowledge Distillation: This involves summarization or rewriting the sentences to reduce the number of tokens and make things more precise and short.
   Example:
   Original: "Explain the process of photosynthesis in detail, including the light-dependent and light-independent reactions, and how this process is crucial for life on Earth."
   Compressed: "Describe photosynthesis: light reactions, dark reactions, importance for life."

2. Encoding: This technique transforms text into a format that we might not be able to comprehend easily, but LLMs can make sense of them.
   Example:
   Original: "The quick brown fox jumps over the lazy dog. This sentence contains every letter of the English alphabet."
   Encoded: “VGhlIHF1aWNrIGJyb3duIGZveCBqdW1wcyBvdmVyIHRoZ…”

3. Filtering: This method removes unnecessary parts of prompts. It keeps only the most relevant information, reducing token count. This can be done at various levels, such as sentences, phrases, or tokens.
   Example:
   Original: "Can you please provide me with a comprehensive and detailed explanation of the fundamental principles of quantum mechanics, including its historical development and key concepts such as superposition, entanglement, and wave-particle duality?"
   Filtered: "Explain quantum mechanics: principles, history, superposition, entanglement, wave-particle duality."

Each of these techniques can significantly reduce the number of tokens in a prompt while preserving its core meaning. The choice of technique depends on your specific use case and the type of information you're working with.

Implementing Prompt Compression

To implement prompt compression, you can use various tools and libraries. One popular option is LLMLingua, developed by Microsoft. It uses advanced techniques to refine prompts into key components.

Here are examples of how you might implement basic versions of our three main compression techniques using Python.

Knowledge Distillation (Simplified example using a pre-trained model)

from transformers import pipeline

def compress_prompt_distill(prompt, max_length=50):
    summarizer = pipeline("summarization", model="facebook/bart-large-cnn")
    summary = summarizer(prompt, max_length=max_length, min_length=10, do_sample=False)
    return summary[0]['summary_text']

original_prompt = "Explain the process of photosynthesis in detail, including the light-dependent and light-independent reactions, and how this process is crucial for life on Earth."
distilled_prompt = compress_prompt_distill(original_prompt)
print(f"Distilled prompt: {distilled_prompt}")

Encoding (Using sentence embeddings)

from sentence_transformers import SentenceTransformer

def compress_prompt_encode(prompt):
    model = SentenceTransformer('all-MiniLM-L6-v2')
    return model.encode(prompt)

original_prompt = "The quick brown fox jumps over the lazy dog."
encoded_prompt = compress_prompt_encode(original_prompt)
print(f"Encoded prompt (first 5 values): {encoded_prompt[:5]}")

Filtering (Keyword Extraction)

import nltk
from nltk.corpus import stopwords
from nltk.tokenize import word_tokenize

nltk.download('punkt')
nltk.download('stopwords')

def compress_prompt_filter(prompt, num_keywords=10):
    words = word_tokenize(prompt.lower())
    stop_words = set(stopwords.words('english'))
    keywords = [word for word in words if word.isalnum() and word not in stop_words]
    freq_dist = nltk.FreqDist(keywords)
    top_keywords = [word for word, _ in freq_dist.most_common(num_keywords)]
    return " ".join(top_keywords)

original_prompt = "Prompt compression is a technique used to optimize inputs given to large language models (LLMs) by reducing their length while maintaining output quality and relevance."
compressed_prompt = compress_prompt_filter(original_prompt)
print(f"Filtered prompt: {compressed_prompt}")

When implementing prompt compression, keep these best practices in mind:

• Maintain a balance between compression and preserving essential information.

• Test compressed prompts to ensure they still produce high-quality outputs.

• Be aware that excessive compression can lead to loss of important nuances.

Common challenges include handling diverse types of input data and ensuring consistent output quality. Overcome these by testing your compression methods on a variety of prompts and fine-tuning your approach.

Case studies often show significant cost reductions, sometimes up to 50% or more, without major impact on output quality. However, results can vary based on the specific use case and compression method.

The field of prompt compression is rapidly evolving. New techniques are emerging, such as dynamic compression ratios and context-aware compression. In the future, we may see prompt compression integrated directly into LLM architectures, making it even more efficient and effective.

By implementing prompt compression, you can significantly reduce your LLM token costs while maintaining the quality of your AI applications. Start with simple techniques and gradually explore more advanced methods as you become comfortable with the concept.