Large Language Model Glossary
Key terms and concepts in LLM technology: tokens, context windows, temperature, RAG, fine-tuning, quantization, KV cache, and emergence.
The data
Terms
| Term | Definition |
|---|---|
| Token | A unit of text processed by LLMs, typically a word fragment or punctuation |
| Context Window | Maximum number of tokens an LLM can process in a single inference |
| Temperature | Sampling parameter controlling randomness in generation (0 = deterministic, >1 = creative) |
| RAG | Retrieval-Augmented Generation: combining LLMs with external knowledge retrieval |
| Fine-tuning | Adapting a pre-trained model to specific tasks with additional training |
| Quantization | Reducing model precision (e.g., FP16 to INT8) to decrease size and increase speed |
| KV Cache | Key-Value cache storing attention computations for efficient autoregressive generation |
| Emergence | Capabilities that appear only at certain model scales, not predictable from smaller models |
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