LLMs are costly, and the number of tokens can rapidly increase due to the costs. Yet these potent tools need not be left behind. They can utilize strategic text compression to make their content more effective. You can cut AI costs by almost 30-70% and still preserve quality by simply sending the AI models a smaller volume of text, while maintaining the relevant information.

Understanding LLM Token Costs

It is essential to understand how LLM pricing operates before exploring compression techniques. The token, or unit of text that carries approximately equal value to a word or part of a word, is generally the unit of charge at most AI providers. An average English word is equivalent to approximately 1.3 tokens, so that a 1000-word text message would have a processing cost of roughly 1300 tokens.

There are numerous popular models, such as GPT-4, that have different token charges for input and output. Input tokens (your prompts and background) are generally cheaper than output tokens (what the AI provides you with). This pricing model allows optimizing input especially well because you are including context with each request.

Using tokens will quickly accumulate in the production environment. A customer service robot that handles 10,000 requests per day with context windows of 500 words would earn 6.5 million tokens in a day. This amounts to huge monthly costs at existing price levels, which compression can mitigate.

Core Text Compression Techniques

Remove Redundant Information

The simplest compression technique is to remove redundant textual data. Eliminate repetitive sentences, superfluous descriptions, and empty filler words. Emphasize maintaining fundamental ideas as you de-brief with a sheet of verbal instructions.

e.g., rather than writing: The customer is very frustrated and angry that the order has not been shipped yet, when it should have arrived yesterday, parsimoniously compress to: Customer angry X at order not being shipped yet.

Utilize Abbreviations and Acronyms

Strategic abbreviation minimizes the use of tokens while still being understandable. Use standard abbreviations in place of commonly used phrases: customer service should be abbreviated as CS, a return merchandise authorization as RMA, and most frequently asked questions should be shortened as FAQ.

Standardize ways of abbreviating terms used in your domain. Record these abbreviations so that other team members are aware of the compression conventions you have established.

Implement Structured Data Formats

Formatted data can be effectively and correctly used to communicate robust information to other systems. These formats consist of a standard framework to define and present data that is simpler to compute and investigate.

A common structured data format is often used in web development. It provides a key-value pair format to store data, which is simple to read and write by humans, and parse and create by machines.

XML (Extensible Markup Language) is another type of structured data format often used in enterprise software systems. XML is also a hierarchical language with tags to describe data elements, and therefore capable of controlling more complex data or any variable data.

Advanced Compression Strategies

Context Window Optimization

Many LM applications send too much context per request. Nine out of Ten Do analyze your use cases and identify the least useful context in which you can give accurate responses. Understand sliding window techniques that handle only the most recent pertinent interactions, rather than complete histories of conversations.

In chatbots, it is also advisable to summarize older sections of conversation rather than providing full transcripts. This maintains continuity, both in terms of long chat histories and overall continuity.

Semantic Compression

Concentrate on saving meaning and ruthlessly cut down words. In this technique, you must comprehend what information elements you need most (in your particular use case). Customer service software can be designed to prioritize the description of issues over niceties, much like content-generating software prioritizes the important points over the supporting ones.

To exercise semantic compression, keep the main message of a longer text and reread it to try to store this meaning in fewer words. This is an area that improves with practice and knowledge in the field.

Template-Based Compression

Create standard input format templates to standardize input responses. Templates reduce variability and tend to cause shorter and narrower prompts. Prepare templates based on common scenarios or the type of mail received, such as customer complaints, product queries, or content demand emails.

Templates also lead to a higher level of consistency of responses as the LLM is fed similarly structured inputs. Such uniformity can both improve the quality of output and minimize the cost of processing.

Preprocessing and Automation Tools

Text Preprocessing Scripts

Construct neural auto-preprocessing pipelines that replicate the compressing text and then feed the results into the LLMs. These scripts can support common compression operations such as stop word elimination, format standardization, and domain-specific abbreviations.

Python packages such as NLTK and spaCy provide excellent bases for developing your own preprocessing software. You can start by using simple, clean operations and add custom compression operations as needed.

API Wrappers with Compression

Write wrapper functions for your API calls to the LLM that automatically compress data. These wrappers may also employ different compression strategies, allowing you to experiment with them and determine which methods are effective and which are not.

Alternatively, instead of a single type setting, you might also want to have compression level settings, such as light, medium, and aggressive, that use a combination of techniques in varying degrees depending on your quality and cost preferences.

Batch Processing Optimization

Whenever possible, consolidate multiple requests to reduce overhead. There are several compression advantages, including the removal of repetition within individual requests. Batching can also help you take advantage of bulk pricing levels provided by select LLM providers.

Measuring Compression Effectiveness

Monitor key statistics to ensure that your compression policies provide value without compromising the quality of output. Measure the percentage changes in monitoring tokens, cost savings, and response accuracy among various degrees of compression.

Take baseline measurements before compression and compare the results of all compression techniques. Certain compression schemes are not generally well-suited to any particular application, so experimentation can be used to determine the best option for your program.

Consider A/B testing compressed prompts versus uncompressed prompts on a portion of traffic to verify that compression does not have adverse effects on user experience or business results.

Implementation Best Practices

Begin with conservative methods of compression and gradually increase aggressiveness as you become more confident in your approach. When using semantic compression, start with the easy tasks, such as eliminating redundant text and abstracting formatting, before addressing the underlying semantic elements.

Record your compression plans to ensure side-by-side consistency between team members and applications. Establish a set of rules to define when and how to apply the various degrees and methods of compression.

Monitor model performance closely when implementing compression. Other destructive compression methods may be cost-effective, but they result in lower quality output, thereby creating a false economy that can lead to further processing requirements.

Conclusion

Text compression is a simple yet powerful way to cut LLM costs without losing functionality. By reducing redundancy, standardizing formats, and applying advanced techniques, organizations can significantly reduce the cost of API calls. Compression is an ongoing process—regularly review strategies as your needs and technologies evolve. Even small improvements in token efficiency can drive major savings, freeing up resources to expand AI capabilities.