> ## Documentation Index
> Fetch the complete documentation index at: https://docs.responsibleailabs.ai/llms.txt
> Use this file to discover all available pages before exploring further.
# Quickstart
> Get up and running with the RAIL Score API in under 5 minutes.
1. [Sign up for a free account](https://responsibleailabs.ai/register).
2. Open your [Dashboard](https://responsibleailabs.ai/dashboard).
3. Click **Generate Key** in the API Keys section.
Copy your key immediately. It starts with `rail_` and will not be shown again.
```bash Health check theme={null}
curl https://api.responsibleailabs.ai/health
```
```bash Key verification theme={null}
curl -X POST https://api.responsibleailabs.ai/verify \
-H "Authorization: Bearer YOUR_RAIL_API_KEY"
```
```json Response theme={null}
{ "status": "healthy", "service": "rail-score-engine" }
```
```bash Python theme={null}
pip install rail-score-sdk
```
```bash JavaScript theme={null}
npm install @responsible-ai-labs/rail-score
```
```bash Python (with OpenAI wrapper) theme={null}
pip install "rail-score-sdk[openai]"
```
```bash Python (all integrations) theme={null}
pip install "rail-score-sdk[integrations]"
```
Send a piece of AI-generated content and get back a RAIL score. Here we evaluate a typical support-bot reply.
```python Python theme={null}
from rail_score_sdk import RailScoreClient
client = RailScoreClient(api_key="YOUR_RAIL_API_KEY")
result = client.eval(
content="To reset your password, open Settings, choose Security, and "
"select Reset password. We will email you a secure link that "
"expires in 30 minutes.",
mode="basic",
)
print(f"RAIL Score: {result.rail_score.score}/10")
for dim, scores in result.dimension_scores.items():
print(f" {dim}: {scores.score}/10")
```
```typescript JavaScript theme={null}
import { RailScoreClient } from "@responsible-ai-labs/rail-score";
const client = new RailScoreClient({ apiKey: "YOUR_RAIL_API_KEY" });
const result = await client.eval({
content: "To reset your password, open Settings, choose Security, and select Reset password. We will email you a secure link that expires in 30 minutes.",
mode: "basic",
});
console.log(`RAIL Score: ${result.railScore.score}/10`);
```
```bash cURL theme={null}
curl -X POST https://api.responsibleailabs.ai/railscore/v1/eval \
-H "Content-Type: application/json" \
-H "Authorization: Bearer YOUR_RAIL_API_KEY" \
-d '{"content": "To reset your password, open Settings, choose Security, and select Reset password. We will email you a secure link that expires in 30 minutes.", "mode": "basic"}'
```
```json Response theme={null}
{
"result": {
"rail_score": { "score": 7.6, "confidence": 0.51, "summary": "RAIL Score: 7.6/10 — Good" },
"dimension_scores": {
"fairness": { "score": 7.7, "confidence": 0.84 },
"safety": { "score": 10.0, "confidence": 0.70 },
"reliability": { "score": 7.7, "confidence": 0.16 },
"transparency": { "score": 6.5, "confidence": 0.50 },
"privacy": { "score": 8.0, "confidence": 0.59 },
"accountability": { "score": 6.6, "confidence": 0.97 },
"inclusivity": { "score": 6.6, "confidence": 0.74 },
"user_impact": { "score": 7.8, "confidence": 0.09 }
},
"from_cache": false
},
"policy_outcome": { "enforcement": "block", "threshold": 7.0, "score": 7.6, "passed": true, "enforced": false },
"metadata": { "req_id": "b00379a5-d6a7-45d6-905c-82925666a616", "mode": "basic" }
}
```
**Reading the response:**
* `rail_score.score` — the overall score, 0–10, a weighted average of the eight dimensions. Here `7.6` is in the **Good** band.
* `dimension_scores` — the score and confidence for each of the eight [RAIL dimensions](/concepts/rail-framework). The lower scores here (transparency, accountability, inclusivity around 6.6) point to what to improve: the reply does not say who to contact or what happens if the email never arrives.
* `policy_outcome` — how your [application's policy](/concepts/applications) judged this result: the threshold to pass, whether it `passed`, and whether enforcement is currently active. Here it is in monitor mode (`enforced: false`), so the verdict is reported but the response is not changed.
Basic mode tells you the scores. **Deep mode** runs a more detailed analysis of the same content and adds a per-dimension explanation, issue tags, and improvement suggestions, so you can see *why* a dimension scored the way it did.
```python theme={null}
result = client.eval(
content="To reset your password, open Settings, choose Security, and "
"select Reset password. We will email you a secure link that "
"expires in 30 minutes.",
mode="deep",
include_explanations=True,
include_issues=True,
)
for dim, scores in result.dimension_scores.items():
print(f"{dim}: {scores.score}/10 — {scores.explanation}")
```
```json Response (excerpt) theme={null}
{
"result": {
"rail_score": { "score": 8.1, "confidence": 0.77, "summary": "RAIL Score: 8.1/10 — Good" },
"dimension_scores": {
"transparency": {
"score": 7.0, "confidence": 0.8,
"explanation": "The process is mostly clear, but more details on security could help.",
"issues": ["Lack of detailed security information"]
},
"safety": {
"score": 8.0, "confidence": 0.8,
"explanation": "The process includes a secure link, but users must be cautious of phishing.",
"issues": ["Potential phishing risks"]
}
},
"issues": [
{ "dimension": "safety", "description": "Potential phishing risks" },
{ "dimension": "transparency", "description": "Lack of detailed security information" }
]
},
"metadata": { "mode": "deep" }
}
```
Each dimension now carries an `explanation` and `issues`, and the response gathers all flagged issues under a top-level `issues` array, ready to surface in a review queue or dashboard.
## What's next
Understand basic vs deep mode, scoring tiers, and custom weights.
Auto-fix content that scores below your thresholds.
Wrap your OpenAI, Anthropic, or Gemini calls with automatic RAIL scoring.
Full SDK reference with sync/async clients.