> For the complete documentation index, see [llms.txt](https://docs.scorable.ai/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.scorable.ai/overview/concepts.md).
# Concepts
Scorable is built on a few core concepts that help you organize your evaluation strategy.
**Objective** defines *what* you want to achieve (e.g., "Ensure the bot is polite").
[**Evaluator**](https://docs.scorable.ai/usage/usage/evaluators) is a specific check that scores a piece of text (0-1) based on that objective. Think of it as a **unit test for semantics**.
[**Judge**](https://docs.scorable.ai/usage/usage/judges) is a collection of evaluators bundled together for a specific use case. It represents your **definition of quality** for a specific task.
[**Model**](https://docs.scorable.ai/usage/usage/models) is the AI model such as an LLM that provides the semantic processing of the inputs. Notably, the list contains both API-based models such as OpenAI and Anthropic models, and open source models such as Llama and Mistral models. Finally, you can add your own locally running models to the list [with ease](/concepts-and-examples/cookbooks/connect-a-model.md). The organization Admin controls the availability of models enabled in your organization.
[**Dataset**](https://docs.scorable.ai/usage/usage/datasets-and-annotations) is a collection of request and response examples. Annotated with expected scores, datasets calibrate your evaluators and steer them via demonstrations.
[**Project**](https://docs.scorable.ai/usage/usage/projects) is a workspace inside your organization that groups related judges, evaluators, datasets, and execution logs under a single label. Use projects to keep, for example, a staging app and a production app cleanly separated.