Large Language Model Glossary
Key terms and concepts in LLM technology: tokens, context windows, temperature, RAG, fine-tuning, quantization, KV cache, and emergence.
A glossary of large-language-model terms with precise definitions: tokens, context windows, temperature, retrieval-augmented generation, fine-tuning, quantization, KV cache and emergence, extended with embeddings, prompts, hallucination, sampling methods, RLHF, tool use, agents, mixture of experts, distillation, LoRA, perplexity and prompt injection.
Use it when
- You need a one-sentence definition of an LLM term for documentation or a prompt.
- You are aligning a team on shared vocabulary.
Watch out for
- The field changes fast; definitions here are conceptual and avoid claims about specific models.
- Some terms (agent, emergence) are used loosely and with different meanings across papers and products.
- Temperature zero does not guarantee reproducibility across hardware, serving systems, or model revisions.
The data
Terms
41 rows
| 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 | A parameter that rescales sampling logits; lower values concentrate probability. Zero-temperature behavior and reproducibility depend on the implementation. |
| 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 | A capability whose measured performance appears to change sharply with scale; the appearance can depend on the metric and evaluation design. |
| Embedding | A dense vector representing text or another input so that semantically similar items are close in vector space |
| Prompt | The input text, including instructions and examples, given to a model to elicit a response |
| System Prompt | Instructions set by the application developer that frame the model's role and behavior for a conversation |
| Hallucination | Output that is fluent and confident but false or unsupported by the provided sources |
| Top-p (Nucleus Sampling) | Sampling from the smallest set of tokens whose cumulative probability exceeds p, which adapts the candidate set to the distribution |
| Top-k Sampling | Sampling from only the k most probable next tokens |
| Inference | Running a trained model to produce outputs, as opposed to training it |
| Pretraining | The initial large-scale training of a model on broad data to learn general patterns before specialization |
| Instruction Tuning | Fine-tuning a model on examples of instructions and desired responses so it follows directions |
| RLHF | Reinforcement learning from human feedback: tuning a model using human preference judgments as a reward signal |
| Chain-of-Thought | Prompting or training a model to produce intermediate reasoning steps before a final answer |
| Few-Shot Prompting | Including a handful of worked examples in the prompt to show the desired task format |
| Zero-Shot | Performing a task from instructions alone, with no examples in the prompt |
| In-Context Learning | A model's ability to adapt to a task from examples or instructions in the prompt without updating its weights |
| Tool Use (Function Calling) | A model emitting structured calls to external functions or APIs, whose results are returned to it |
| Agent | A system in which a model repeatedly decides on actions, uses tools and observes results to pursue a goal |
| Mixture of Experts | An architecture that routes each token to a few specialized sub-networks, increasing capacity without activating all parameters |
| Distillation | Training a smaller model to imitate the outputs of a larger one |
| LoRA | Low-rank adaptation: fine-tuning by training small added matrices while keeping the base weights frozen |
| Perplexity | A measure of how well a language model predicts text; lower means the text is less surprising to the model |
| Prompt Injection | An attack that places instructions in untrusted input so the model follows them in place of the developer's |
| Guardrails | Checks and constraints around a model that filter inputs and outputs to keep behavior within policy |
| Multimodal | Able to process more than one kind of input or output, such as text, images and audio |
| Vector Database | A store optimized for nearest-neighbor search over embeddings |
| Chunking | Splitting documents into pieces of suitable size for embedding and retrieval |
| Logits | The raw, unnormalized scores a model produces for each possible next token before softmax |
| Alignment | The effort to make model behavior match human intentions and values |
| Scaling Laws | Empirical relationships showing how model loss improves predictably with more parameters, data and compute |
| Model Context Protocol (MCP) | An open protocol that standardizes how applications give models access to tools and data sources |
| Groundedness | Whether answer claims are supported by the supplied evidence; it differs from whether the evidence itself is correct. |
| Calibration | Agreement between stated or estimated confidence and observed correctness over a set of predictions. |
| Held-out Evaluation | Assessment on cases withheld from development choices, to reduce optimistic bias from repeated tuning. |
| Abstention | Declining to answer when the evidence or confidence is insufficient, according to an explicit policy. |
Fields
The shape of this dataset, read from the data itself: every column, its type and a real value. Agents can use this to plan a query without fetching the whole document.
| Section | Field | Type | Example |
|---|---|---|---|
| terms | term | string | Token |
| terms | definition | string | A unit of text processed by LLMs, typically a w… |
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Sources and editorial notes
Original editorial reference notes by YJTOON, informed by the linked primary documentation. External specifications retain their own licenses.
- Stanford HELM evaluation framework
- Hugging Face generation parameters
- Schaeffer et al. on emergence and metric choice
Editorial review: . Source documents keep their own licenses.