Combining Context Engineering With Lightweight Compute

Making Semantic Text Management Lightweight

Much has been written about the ability of large language models (LLMs) to spin up large amounts of text, and still more is being written about how an overreliance on AI-generated text might do more harm than good. However, natural language processing (NLP) is a core part of the functionality of LLMs.

AI models require strong semantic text management capabilities to clearly understand natural-language prompts and to return accurate, intelligible responses. While training massive models can be compute intensive, it’s also possible to build lightweight NLP tools optimized to run on CPU, like WordLlama. The project’s creator, physics researcher-turned-data-scientist and engineer Lee Miller explains.

WordLlama is a lightweight NLP toolkit designed to run on CPU compute.

Tackling Token Economics and Training Costs With Embedding

Miller explains that the project came about from a few observations: One, that embedding packages for word embeddings for models with moderately large, million-word vocabularies can add up to a huge data footprint; and two, that LLMs use much smaller token-level vocabularies but are trained on trillions of tokens, forming strong semantic representations, useful for many NLP tasks.

Miller suggests his priorities were about making a project that was both as lightweight and as performant as possible. “I wanted to make sure the project didn’t have a lot of dependencies, such as requiring users to install PyTorch. Those can be pretty big packages that add a lot of compute overhead, especially if you’re considering any form of attention.”

“So I had to consider: How simple can we make the operations of doing a semantic sorting technique? In other words, can we call a sort operation on a list of strings but do it semantically (by word meaning) rather than alphabetically?” The creator explains that his goal was to avoid deep learning interfaces with high overhead altogether, converting everything into minimal formats like NumPy and Safetensors instead.

Getting NLP Projects to Work in Low-Compute Environments

Miller suggests that at least for the time being, there’s a stark difference between LLM projects built for CPU compute and those built for GPU. “If you start looking into GPU compute for your projects, it doesn't make sense to do things that are this lightweight anymore. It's a completely different area of computing altogether.”

The creator explains that in some ways, his goal was more about optimizing for low-compute operations rather than top-of-the-line performance. “Quite honestly, the model I’m using isn’t necessarily the best way to do what this library does, but it can do what it was designed to do without all the overhead that a lot of the bigger models require.”

Lightweight NLP and Semantic Text Management Use Cases

For example, WordLlama can perform fuzzy de-duplication, finding and removing two text strings that may be similar but differ in unanticipated ways, such as different versions of the same customer or lead across a startup’s sales records. “You could do a great job with a cross encoder or another transformer model–but there are other use cases that don’t need all that horsepower.”

The creator notes that users apply the project with RAG systems to process small text snippets for list sorting, or other data processing-related tasks, such as fuzzy deduplication, text clustering, or semantic text splitting by way of fuzzy matching, such as for sorting semantically similar custom survey responses.

“The idea is to add that little bit of a semantic layer to all the different Python operations that you might want to run in order to tease things out based on their semantic meaning, rather than the physical structure of the text string.”

Having really lightweight tools can be important in a lot of applications where all you really need is to do something semantically relevant to your LLM interaction. Ultimately, it’s another tool in your toolbox for helping you get good outputs from your LLM.” Lee Miller, Creator/WordLlama

The Future Value of NLP and Lightweight Compute

“One thing that I've found working with LLMs is that the more that you start doing with NLP, the more that you need a variety of tooling to help you utilize it effectively,” Miller notes. “Anything from ways to evaluate a model’s output to ways to provide the right context (or what I guess is now called context engineering)...these things determine what should go into the model on your next inference turn.”

The creator suggests that working with NLP use cases has also underscored the tradeoffs between relying on lightweight vs. heavyweight compute. “You might want to make different choices based on your application performance needs. LLM inference isn’t the fastest thing in the world, and you don’t want additional latency where you don’t necessarily need it.”

“It can come down to whether someone is using an LLM from their laptop to do context engineering, or some evaluation on the output. If they don’t have a GPU on their computer, do they really want to have to go load up something externally on GPU hardware just to do some basic semantic work on their inputs or outputs?”

“Having really lightweight tools can be important in a lot of applications where all you really need is to do something semantically relevant to your LLM interaction. Ultimately, it’s another tool in your toolbox for helping you get good outputs from your LLM. Otherwise, there’s a lot of opportunities in terms of efficiency and performance, where you don't necessarily want to throw the whole kitchen sink at the LLM, and can easily weed out things that might not be relevant for the context. And that's really where the context engineering piece comes in.”

How Founders Should Think About NLP and Future Developments

Miller cautions founders to beware the hidden costs of going straight to top-of-the-line LLMs. “There can be a tendency to go with the biggest model you can get, with the highest accuracy you can find, to try to head straight to the very top end of things.”

“But along the way, there's a lot of decisions you can make around prioritizing the information you put into your models that can really benefit the accuracy and improve your outputs as well. I’d advise teams to look into even some basic context engineering, or semantic splitting to filter out things you aren't going to want to put in your context.”

“These are what I might call ‘NLP-lite’ tasks. Depending on your use case, you don't necessarily need anything too heavy-duty for a lot of those in terms of models and compute. But looking into a package like this can really help with improving inputs to your LLM. So if you're working on building with AI, it’s a good idea to keep in mind that token input cost is only part of the picture. You can use context engineering to both reduce input costs and improve outputs by targeting more relevant information."