> 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/concepts-and-examples/usage/objectives.md).

# Objectives

Objectives consist of a human-readable Intent and ground truth examples. An objective serves both the purposes of

* Communication: Expressing the intended business purpose of the evaluator
* Coordination: Serving as a battery of measures

## Intent Structure

Scorable uses a standardized intent structure that bridges human-readable descriptions with machine-understandable syntax. This universal format ensures semantic consistency across all evaluators while maintaining clarity for human interpretation.

### Standard Format

<mark style="background-color:blue;">**Property**</mark> \[of <mark style="background-color:green;">**Object types**</mark> \[with respect to <mark style="background-color:purple;">**Reference objects**</mark>]] \[in <mark style="background-color:orange;">**Context**</mark>] \[for <mark style="background-color:red;">**Goal**</mark>] \[with weights <mark style="background-color:yellow;">**level1**</mark> for a₁, b₁, c₁ \[; <mark style="background-color:yellow;">**level2**</mark> for a₂, b₂, c₂]]

### Components

* <mark style="background-color:blue;">**Property**</mark>: The single quality being measured (e.g., Relevance, Safety, Coherence)
* <mark style="background-color:green;">**Object types**</mark>: The text artifacts being evaluated (response, content, answer, JSON)
* <mark style="background-color:purple;">**Reference objects**</mark>: What the evaluation compares against (request, prompt, ground truth)
* <mark style="background-color:orange;">**Context**</mark>: Specific situational constraints (child-audience, professional, RAG evaluation)
* <mark style="background-color:red;">**Goal**</mark>: The desired outcome (keeping responses on-topic, age-appropriate consumption)
* <mark style="background-color:yellow;">**Weight levels**</mark>: Criteria importance (high, avoiding, detecting)

### Examples

**Relevance Evaluator:**

> <mark style="background-color:blue;">**Relevance**</mark> of <mark style="background-color:green;">**response**</mark> with respect to <mark style="background-color:purple;">**request**</mark> for <mark style="background-color:red;">**keeping responses on-topic and informative**</mark> with weights <mark style="background-color:yellow;">**high**</mark> for accuracy, completeness, adherence to prompt, logical consistency

**Safety for Children:**

> <mark style="background-color:blue;">**Safety**</mark> of <mark style="background-color:green;">**content**</mark> in <mark style="background-color:orange;">**child-audience context**</mark> for <mark style="background-color:red;">**age-appropriate consumption**</mark> with weights <mark style="background-color:yellow;">**avoiding**</mark> for explicit language, violent content, adult themes

**Conciseness:**

> <mark style="background-color:blue;">**Conciseness**</mark> of <mark style="background-color:green;">**response**</mark> for <mark style="background-color:red;">**efficient communication**</mark> with weights <mark style="background-color:yellow;">**high**</mark> for brevity, directness ; <mark style="background-color:yellow;">**avoiding**</mark> for redundancy

### JSON Representation

The structured format also translates to machine-readable JSON:

```json
{
  "property": "Relevance",
  "object": "response",
  "respect_to": "request",
  "goal": "keeping responses on-topic and informative",
  "weights": {
    "high": ["accuracy", "completeness", "adherence to prompt", "logical consistency"]
  }
}
```

This standardized approach ensures that every objective intent is both semantically precise and universally interpretable across different contexts and implementations.