> ## Documentation Index
> Fetch the complete documentation index at: https://docs.inference.net/llms.txt
> Use this file to discover all available pages before exploring further.

# Glossary

> Quick-reference definitions for Catalyst concepts and terminology.

| Term                  | Definition                                                                                                                                        |
| --------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------- |
| **Task**              | A user-defined objective that groups LLM calls (e.g., "summarize document," "classify ticket"). Tasks persist even as the implementation changes. |
| **Rubric**            | A plain English description of how to judge model output on a quality dimension. Scored numerically by an LLM judge.                              |
| **Recipe**            | A pre-configured training setup including base model, training parameters, and compute config.                                                    |
| **Dataset**           | A curated set of inference samples used for evaluation or training.                                                                               |
| **Eval dataset**      | A dataset used to measure model quality. Should remain stable over time. Must not overlap with training data.                                     |
| **Training dataset**  | A dataset the model learns from during fine-tuning. Evolves as you iterate on data quality.                                                       |
| **Inference**         | A single LLM request-response pair captured by the platform.                                                                                      |
| **Deployment**        | A trained model running on a dedicated GPU, accessible via an OpenAI-compatible API.                                                              |
| **Mid-training eval** | A periodic evaluation run during training that scores model checkpoints against the rubric.                                                       |
| **LLM-as-a-judge**    | The evaluation mechanism where an LLM scores model outputs against rubric criteria.                                                               |
| **TTFT**              | Time to first token. Measures streaming responsiveness.                                                                                           |
| **Overfitting**       | When a model memorizes training data instead of learning generalizable patterns. Detected by degrading eval scores.                               |
| **Distillation**      | Training a smaller model to replicate the quality of a larger model, reducing cost and latency.                                                   |