Concept: Evaluation | Python:
client.eval()Parameters
string
required
The AI-generated text to evaluate. Must be 10–10,000 characters.
string
default:"basic"
Evaluation mode:
"basic"— RAIL’s core scoring models for fast, real-time scoring."deep"— a deeper, more detailed analysis that can also return per-dimension explanations and issue tags."auto"— runsbasic, and automatically escalates todeeponly when a real issue is detected (a low-scoring or low-confidence dimension, or a flagged signal). You get fast scoring on clean content and deep analysis exactly where it matters. When adimensionssubset is requested, only those dimensions can trigger the escalation (and its billing). The responseresultincludesresolved_mode("basic"or"deep"— which tier actually ran) andescalated(boolean). Billed at the tier that ran.
string[]
Subset of dimensions to score. Omit to score all 8. Options:
fairness, safety, reliability, transparency, privacy, accountability, inclusivity, user_impact.object
Custom dimension weights. Values must sum to 100. E.g.
{"safety": 25, "reliability": 20, ...}.string
Domain context hint:
"general", "healthcare", "legal", "finance", "code". Improves scoring accuracy.boolean
default:"false"
Include per-dimension explanations (deep mode only).
boolean
default:"false"
Include detected issue tags per dimension (deep mode only).
boolean
default:"false"
Include improvement suggestions per dimension (deep mode only).
Request
Response
object
How your application’s policy judged this result.
enforcement— the policy’s mode (log_only,block, orregenerate).threshold— the overall score required to pass.score— this result’s overall score.passed— whether the score met the threshold.enforced— whether the outcome was acted on. Whenfalse, the policy is in monitor mode: the verdict is reported but the response is not altered, so you can see what would be blocked. Check the live state withGET /config.
block policy returns 422 POLICY_BLOCKED and a regenerate policy attempts a safe rewrite before applying its fallback.number
Overall RAIL score (0.0–10.0), weighted average of all evaluated dimensions.
number
Model confidence in the score (0.0–1.0).
object
Per-dimension scores. Each entry has
score (0–10) and confidence (0–1). In deep mode: also explanation, issues, suggestions.boolean
true if this result was returned from cache (0 credits charged).string
For
mode: "auto", the tier that actually ran — "basic" or "deep". Use result.escalated to tell whether the deeper analysis was invoked.boolean
Present (as
true) only when the submitted content exceeds the analysis window (about 4,000 characters): the score reflects the leading portion of the content. Split longer content into multiple calls for full coverage.number
Credits charged for this request.
0 for cached responses.