After using an LLM in llama-cpp
to handle local tasks, I started wondering: why couldn't I write some code to handle the queries faster than the LLM inference? This might work for simple prompts, but it would fail on anything complex.
Natural Language Processing is a field of AI, and Large Language Models are a subset of NLP. The main problem with natural languages is that they have a lot of complexity—there are nearly endless edge cases.
So a simple parser written would:
• Work many times faster than an LLM.
• Fail on many prompts that an LLM might correctly handle.
• End up being an excessive amount of work with rather poor results due to the special case handling required.
Using an LLM like Gemma 4 E4B for natural language processing is the obvious solution. It is probably a bad idea to attempt to handle natural languages at all without a model that has trained data, due to the edge cases and quirks of languages.