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Plain-English Glossary Breaks Down Key AI and LLM Engineering Terms

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A practical reference guide published on DEV Community aims to demystify the jargon that has rapidly built up around AI and large language model (LLM) engineering. The glossary targets backend engineers, explaining terms at a working level rather than academic or marketing language. It covers foundational concepts such as tokens, context windows, inference, and model parameters, as well as memory and performance-related topics like KV caching, quantization, and attention mechanisms. The guide also addresses efficiency techniques including LoRA fine-tuning and grouped-query attention, which are commonly encountered in production AI systems. Its stated goal is to serve as a single, organized reference for terms that engineers regularly encounter across research papers, vendor documentation, and real-world deployments.

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