{
"evaluator_templates": [
{
"column_name": "hallucination",
"display_name": "Hallucination",
"template": "You are evaluating whether an answer is factual given reference text.\n\n[Input]: {input}\n[Reference]: {context}\n[Answer]: {output}\n\nRespond with a single word: factual or hallucinated.",
"rails": [
"hallucinated",
"factual"
],
"classification_choices": {
"hallucinated": 1,
"factual": 0
},
"direction": "MINIMIZE",
"data_granularity": "SPAN"
},
{
"column_name": "session_frustration",
"display_name": "Session Frustration",
"template": "You are given a multi-turn session between a user and an AI assistant.\n\n{conversation}\n\nRespond with a single word: frustrated or ok.",
"rails": [
"frustrated",
"ok"
],
"classification_choices": {
"frustrated": 1,
"ok": 0
},
"direction": "MINIMIZE",
"data_granularity": "SESSION"
}
]
}
{
"status": 401,
"title": "Authentication required",
"detail": "You must be authenticated to access this resource.",
"instance": "/resource",
"type": "https://arize.com/docs/ax/rest-reference/errors#authentication-required"
}
{
"status": 403,
"title": "Access forbidden",
"detail": "You do not have permission to access this resource.",
"instance": "/resource/12345",
"type": "https://arize.com/docs/ax/rest-reference/errors#access-forbidden"
}
{
"status": 429,
"title": "Rate limit exceeded",
"detail": "You have exceeded the allowed number of requests. Please try again later.",
"instance": "/resource",
"type": "https://arize.com/docs/ax/rest-reference/errors#rate-limit-exceeded"
}
Evaluators
List evaluator templates
Retrieve the built-in LLM-as-a-judge evaluator templates. This is the same
catalog the product offers when creating an evaluator, spanning response
quality, code quality, trajectory, RAG, security, and session evals.
Each template carries the judge prompt, the labels it returns, the score
for each label, its optimization direction, and the granularity it
evaluates at.
Creating an evaluator from a template. Pick a template, then call
POST /v2/evaluators with its fields mapped onto the request:
Template field
Where it goes in POST /v2/evaluators
column_name
version.template_config.name
template
version.template_config.template
classification_choices
version.template_config.classification_choices
direction
version.template_config.direction
data_granularity
version.template_config.data_granularity. Send SPAN, or omit it, when the templateās value is null
display_name
a label for your own use; reuse it for the evaluatorās name or description
rails
no destination; classification_choices already carries the same labels
Then add the fields a template doesnāt carry: space_id, name, and
type: TEMPLATE on the evaluator; a version.commit_message; and the
execution settings template_config.include_explanations,
use_function_calling_if_available, and llm_config. Finally, create a
task to run the evaluator.
A complete request built from the hallucination template:
{ "space_id": "U3BhY2U6NDkzOkJaSkc=", "name": "hallucination", "description": "Built from the hallucination template", "type": "TEMPLATE", "version": { "commit_message": "Initial version from built-in template", "template_config": { "name": "hallucination", "template": "You are evaluating whether an answer is factual given reference text...\n{input}\n{output}", "classification_choices": { "hallucinated": 1, "factual": 0 }, "direction": "MINIMIZE", "data_granularity": "SPAN", "include_explanations": true, "use_function_calling_if_available": true, "llm_config": { "ai_integration_id": "TGxtSW50ZWdyYXRpb246MTI6YUJjRA==", "model_name": "gpt-4o", "invocation_parameters": { "temperature": 0 }, "provider_parameters": {} } } }}
Scope: this returns only the built-in catalog, which is identical for
every caller and contains no space, account, or user data. It does not
include the evaluators that already exist in your space. List those with
GET /v2/evaluators.
Pagination: not paginated. The catalog is a small fixed list (28
templates, roughly 32 KB of JSON) and the full set is always returned, so
there is no cursor or limit.
{
"evaluator_templates": [
{
"column_name": "hallucination",
"display_name": "Hallucination",
"template": "You are evaluating whether an answer is factual given reference text.\n\n[Input]: {input}\n[Reference]: {context}\n[Answer]: {output}\n\nRespond with a single word: factual or hallucinated.",
"rails": [
"hallucinated",
"factual"
],
"classification_choices": {
"hallucinated": 1,
"factual": 0
},
"direction": "MINIMIZE",
"data_granularity": "SPAN"
},
{
"column_name": "session_frustration",
"display_name": "Session Frustration",
"template": "You are given a multi-turn session between a user and an AI assistant.\n\n{conversation}\n\nRespond with a single word: frustrated or ok.",
"rails": [
"frustrated",
"ok"
],
"classification_choices": {
"frustrated": 1,
"ok": 0
},
"direction": "MINIMIZE",
"data_granularity": "SESSION"
}
]
}
{
"status": 401,
"title": "Authentication required",
"detail": "You must be authenticated to access this resource.",
"instance": "/resource",
"type": "https://arize.com/docs/ax/rest-reference/errors#authentication-required"
}
{
"status": 403,
"title": "Access forbidden",
"detail": "You do not have permission to access this resource.",
"instance": "/resource/12345",
"type": "https://arize.com/docs/ax/rest-reference/errors#access-forbidden"
}
{
"status": 429,
"title": "Rate limit exceeded",
"detail": "You have exceeded the allowed number of requests. Please try again later.",
"instance": "/resource",
"type": "https://arize.com/docs/ax/rest-reference/errors#rate-limit-exceeded"
}
Most Arize AI endpoints require authentication. For those endpoints that require authentication, include your API key in the request header using the format
Every built-in template, ordered by category as the product presents
them (response quality, code quality, trajectory, RAG, security,
session).
Show child attributes
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This is the same\ncatalog the product offers when creating an evaluator, spanning response\nquality, code quality, trajectory, RAG, security, and session evals.\n\nEach template carries the judge prompt, the labels it returns, the score\nfor each label, its optimization direction, and the granularity it\nevaluates at.\n\n**Creating an evaluator from a template.** Pick a template, then call\n`POST /v2/evaluators` with its fields mapped onto the request:\n\n| Template field | Where it goes in `POST /v2/evaluators` |\n| --- | --- |\n| `column_name` | `version.template_config.name` |\n| `template` | `version.template_config.template` |\n| `classification_choices` | `version.template_config.classification_choices` |\n| `direction` | `version.template_config.direction` |\n| `data_granularity` | `version.template_config.data_granularity`. Send `SPAN`, or omit it, when the template's value is `null` |\n| `display_name` | a label for your own use; reuse it for the evaluator's `name` or `description` |\n| `rails` | no destination; `classification_choices` already carries the same labels |\n\nThen add the fields a template doesn't carry: `space_id`, `name`, and\n`type: TEMPLATE` on the evaluator; a `version.commit_message`; and the\nexecution settings `template_config.include_explanations`,\n`use_function_calling_if_available`, and `llm_config`. Finally, create a\ntask to run the evaluator.\n\nA complete request built from the `hallucination` template:\n\n```json\n{\n \"space_id\": \"U3BhY2U6NDkzOkJaSkc=\",\n \"name\": \"hallucination\",\n \"description\": \"Built from the hallucination template\",\n \"type\": \"TEMPLATE\",\n \"version\": {\n \"commit_message\": \"Initial version from built-in template\",\n \"template_config\": {\n \"name\": \"hallucination\",\n \"template\": \"You are evaluating whether an answer is factual given reference text...\\n{input}\\n{output}\",\n \"classification_choices\": { \"hallucinated\": 1, \"factual\": 0 },\n \"direction\": \"MINIMIZE\",\n \"data_granularity\": \"SPAN\",\n \"include_explanations\": true,\n \"use_function_calling_if_available\": true,\n \"llm_config\": {\n \"ai_integration_id\": \"TGxtSW50ZWdyYXRpb246MTI6YUJjRA==\",\n \"model_name\": \"gpt-4o\",\n \"invocation_parameters\": { \"temperature\": 0 },\n \"provider_parameters\": {}\n }\n }\n }\n}\n```\n\n**Scope:** this returns only the built-in catalog, which is identical for\nevery caller and contains no space, account, or user data. It does not\ninclude the evaluators that already exist in your space. List those with\n`GET /v2/evaluators`.\n\n**Pagination:** not paginated. The catalog is a small fixed list (28\ntemplates, roughly 32 KB of JSON) and the full set is always returned, so\nthere is no `cursor` or `limit`.\n\n\u003cWarning>This endpoint is in alpha, read more [here](https://arize.com/docs/ax/rest-reference#api-version-stages).\u003c/Warning>\n","deprecated":null,"version":null,"openapi":"https://api.arize.com/v2/spec.yaml get /v2/evaluator-templates","href":"/api-reference/evaluators/list-evaluator-templates","autogeneratedByOpenApi":true},"description":{"compiledSource":"\"use strict\";\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n const _components = {\n a: \"a\",\n code: \"code\",\n p: \"p\",\n pre: \"pre\",\n span: \"span\",\n strong: \"strong\",\n table: \"table\",\n tbody: \"tbody\",\n td: \"td\",\n th: \"th\",\n thead: \"thead\",\n tr: \"tr\",\n ..._provideComponents(),\n ...props.components\n }, {CodeBlock, Warning} = _components;\n if (!CodeBlock) _missingMdxReference(\"CodeBlock\", true);\n if (!Warning) _missingMdxReference(\"Warning\", true);\n return _jsxs(_Fragment, {\n children: [_jsx(_components.p, {\n children: \"Retrieve the built-in LLM-as-a-judge evaluator templates. 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This is the same catalog the product offers when creating an evaluator, spanning response quality, code quality, trajectory, RAG, security, and session evals. Each template carries the judge prompt, the labels it returns, the score for each label, its optimization direction, and the granularity it evaluates at. Creating an evaluator from a template. Pick a template, then call POST /v2/evaluators with its fields mapped onto the request: | Template field | Where it goes in POST /v2/evaluators | | --- | --- | | column_name | version.template_config.name | | template | version.template_config.template | | classification_choices | version.template_config.classification_choices | | direction | version.template_config.direction | | data_granularity | version.template_config.data_granularity. Send SPAN, or omit it, when the template\u0026#x27;s value is null | | display_name | a label for your own use; reuse it for the evaluator\u0026#x27;s name or description | | rails | no destination; classification_choices already carries the same labels | Then add the fields a template doesn\u0026#x27;t carry: space_id, name, and type: TEMPLATE on the evaluator; a version.commit_message; and the execution settings template_config.include_explanations, use_function_calling_if_available, and llm_config. Finally, create a task to run the evaluator. A complete request built from the hallucination template: json { \u0026quot;space_id\u0026quot;: \u0026quot;U3BhY2U6NDkzOkJaSkc=\u0026quot;, \u0026quot;name\u0026quot;: \u0026quot;hallucination\u0026quot;, \u0026quot;description\u0026quot;: \u0026quot;Built from the hallucination template\u0026quot;, \u0026quot;type\u0026quot;: \u0026quot;TEMPLATE\u0026quot;, \u0026quot;version\u0026quot;: { \u0026quot;commit_message\u0026quot;: \u0026quot;Initial version from built-in template\u0026quot;, \u0026quot;template_config\u0026quot;: { \u0026quot;name\u0026quot;: \u0026quot;hallucination\u0026quot;, \u0026quot;template\u0026quot;: \u0026quot;You are evaluating whether an answer is factual given reference text...\\n{input}\\n{output}\u0026quot;, \u0026quot;classification_choices\u0026quot;: { \u0026quot;hallucinated\u0026quot;: 1, \u0026quot;factual\u0026quot;: 0 }, \u0026quot;direction\u0026quot;: \u0026quot;MINIMIZE\u0026quot;, \u0026quot;data_granularity\u0026quot;: \u0026quot;SPAN\u0026quot;, \u0026quot;include_explanations\u0026quot;: true, \u0026quot;use_function_calling_if_available\u0026quot;: true, \u0026quot;llm_config\u0026quot;: { \u0026quot;ai_integration_id\u0026quot;: \u0026quot;TGxtSW50ZWdyYXRpb246MTI6YUJjRA==\u0026quot;, \u0026quot;model_name\u0026quot;: \u0026quot;gpt-4o\u0026quot;, \u0026quot;invocation_parameters\u0026quot;: { \u0026quot;temperature\u0026quot;: 0 }, \u0026quot;provider_parameters\u0026quot;: {} } } } } Scope: this returns only the built-in catalog, which is identical for every caller and contains no space, account, or user data. It does not include the evaluators that already exist in your space. List those with GET /v2/evaluators. Pagination: not paginated. The catalog is a small fixed list (28 templates, roughly 32 KB of JSON) and the full set is always returned, so there is no cursor or limit. This endpoint is in alpha, read more here.\">\u003cmeta data-mint-head name=\"application-name\" content=\"Arize AX Docs\">\u003cmeta data-mint-head name=\"generator\" content=\"Mintlify\">\u003clink data-mint-head rel=\"icon\" type=\"image/png\" sizes=\"192x192\" href=\"/docs/mintlify-assets/_mintlify/favicons/arize-ax/vxzD83QsbAzzm43O/_generated/favicon/android-chrome-192x192.png\" media=\"(prefers-color-scheme: light)\">\u003clink data-mint-head rel=\"apple-touch-icon\" type=\"image/png\" sizes=\"180x180\" href=\"/docs/mintlify-assets/_mintlify/favicons/arize-ax/vxzD83QsbAzzm43O/_generated/favicon/apple-touch-icon.png\">\u003clink data-mint-head rel=\"icon\" type=\"image/png\" sizes=\"16x16\" href=\"/docs/mintlify-assets/_mintlify/favicons/arize-ax/vxzD83QsbAzzm43O/_generated/favicon/favicon-16x16.png\" media=\"(prefers-color-scheme: light)\">\u003clink data-mint-head rel=\"icon\" type=\"image/png\" sizes=\"32x32\" href=\"/docs/mintlify-assets/_mintlify/favicons/arize-ax/vxzD83QsbAzzm43O/_generated/favicon/favicon-32x32.png\" media=\"(prefers-color-scheme: light)\">\u003clink data-mint-head rel=\"shortcut icon\" type=\"image/x-icon\" href=\"/docs/mintlify-assets/_mintlify/favicons/arize-ax/vxzD83QsbAzzm43O/_generated/favicon/favicon.ico\" media=\"(prefers-color-scheme: light)\">\u003clink data-mint-head rel=\"icon\" type=\"image/png\" sizes=\"192x192\" href=\"/docs/mintlify-assets/_mintlify/favicons/arize-ax/vxzD83QsbAzzm43O/_generated/favicon-dark/android-chrome-192x192.png\" media=\"(prefers-color-scheme: dark)\">\u003clink data-mint-head rel=\"icon\" type=\"image/png\" sizes=\"16x16\" href=\"/docs/mintlify-assets/_mintlify/favicons/arize-ax/vxzD83QsbAzzm43O/_generated/favicon-dark/favicon-16x16.png\" media=\"(prefers-color-scheme: dark)\">\u003clink data-mint-head rel=\"icon\" type=\"image/png\" sizes=\"32x32\" href=\"/docs/mintlify-assets/_mintlify/favicons/arize-ax/vxzD83QsbAzzm43O/_generated/favicon-dark/favicon-32x32.png\" media=\"(prefers-color-scheme: dark)\">\u003clink data-mint-head rel=\"shortcut icon\" type=\"image/x-icon\" href=\"/docs/mintlify-assets/_mintlify/favicons/arize-ax/vxzD83QsbAzzm43O/_generated/favicon-dark/favicon.ico\" media=\"(prefers-color-scheme: dark)\">\u003cmeta data-mint-head property=\"og:title\" content=\"List evaluator templates - Arize AX Docs\">\u003cmeta data-mint-head property=\"og:description\" content=\"Retrieve the built-in LLM-as-a-judge evaluator templates. This is the same catalog the product offers when creating an evaluator, spanning response quality, code quality, trajectory, RAG, security, and session evals. Each template carries the judge prompt, the labels it returns, the score for each label, its optimization direction, and the granularity it evaluates at. Creating an evaluator from a template. Pick a template, then call POST /v2/evaluators with its fields mapped onto the request: | Template field | Where it goes in POST /v2/evaluators | | --- | --- | | column_name | version.template_config.name | | template | version.template_config.template | | classification_choices | version.template_config.classification_choices | | direction | version.template_config.direction | | data_granularity | version.template_config.data_granularity. Send SPAN, or omit it, when the template\u0026#x27;s value is null | | display_name | a label for your own use; reuse it for the evaluator\u0026#x27;s name or description | | rails | no destination; classification_choices already carries the same labels | Then add the fields a template doesn\u0026#x27;t carry: space_id, name, and type: TEMPLATE on the evaluator; a version.commit_message; and the execution settings template_config.include_explanations, use_function_calling_if_available, and llm_config. Finally, create a task to run the evaluator. A complete request built from the hallucination template: json { \u0026quot;space_id\u0026quot;: \u0026quot;U3BhY2U6NDkzOkJaSkc=\u0026quot;, \u0026quot;name\u0026quot;: \u0026quot;hallucination\u0026quot;, \u0026quot;description\u0026quot;: \u0026quot;Built from the hallucination template\u0026quot;, \u0026quot;type\u0026quot;: \u0026quot;TEMPLATE\u0026quot;, \u0026quot;version\u0026quot;: { \u0026quot;commit_message\u0026quot;: \u0026quot;Initial version from built-in template\u0026quot;, \u0026quot;template_config\u0026quot;: { \u0026quot;name\u0026quot;: \u0026quot;hallucination\u0026quot;, \u0026quot;template\u0026quot;: \u0026quot;You are evaluating whether an answer is factual given reference text...\\n{input}\\n{output}\u0026quot;, \u0026quot;classification_choices\u0026quot;: { \u0026quot;hallucinated\u0026quot;: 1, \u0026quot;factual\u0026quot;: 0 }, \u0026quot;direction\u0026quot;: \u0026quot;MINIMIZE\u0026quot;, \u0026quot;data_granularity\u0026quot;: \u0026quot;SPAN\u0026quot;, \u0026quot;include_explanations\u0026quot;: true, \u0026quot;use_function_calling_if_available\u0026quot;: true, \u0026quot;llm_config\u0026quot;: { \u0026quot;ai_integration_id\u0026quot;: \u0026quot;TGxtSW50ZWdyYXRpb246MTI6YUJjRA==\u0026quot;, \u0026quot;model_name\u0026quot;: \u0026quot;gpt-4o\u0026quot;, \u0026quot;invocation_parameters\u0026quot;: { \u0026quot;temperature\u0026quot;: 0 }, \u0026quot;provider_parameters\u0026quot;: {} } } } } Scope: this returns only the built-in catalog, which is identical for every caller and contains no space, account, or user data. It does not include the evaluators that already exist in your space. List those with GET /v2/evaluators. Pagination: not paginated. The catalog is a small fixed list (28 templates, roughly 32 KB of JSON) and the full set is always returned, so there is no cursor or limit. This endpoint is in alpha, read more here.\">\u003cmeta data-mint-head property=\"og:site_name\" content=\"Arize AX Docs\">\u003cmeta data-mint-head property=\"og:url\" content=\"https://arize.com/docs/api-reference/evaluators/list-evaluator-templates\">\u003cmeta data-mint-head property=\"og:type\" content=\"website\">\u003cmeta data-mint-head property=\"og:image\" content=\"https://arize-ax.mintlify.site/docs/_mintlify/api/og/v2?division=Evaluators\u0026amp;title=List+evaluator+templates\u0026amp;description=Retrieve+the+built-in+LLM-as-a-judge+evaluator+templates.+This+is+the+same%0Acatalog+the+product+offers+when+creating+an+evaluator%2C+spanning+response%0Aquality%2C+cod\u0026amp;theme=6b2e1c1a14346acb1262754f\u0026amp;signature=39ab18ef1f3644c8f1ca82e66028cf140de34c710807f6f7438b5a3ffb88dc91\">\u003cmeta data-mint-head property=\"og:image:width\" content=\"1200\">\u003cmeta data-mint-head property=\"og:image:height\" content=\"630\">\u003cmeta data-mint-head name=\"twitter:card\" content=\"summary_large_image\">\u003cmeta data-mint-head name=\"twitter:title\" content=\"List evaluator templates - Arize AX Docs\">\u003cmeta data-mint-head name=\"twitter:description\" content=\"Retrieve the built-in LLM-as-a-judge evaluator templates. This is the same catalog the product offers when creating an evaluator, spanning response quality, code quality, trajectory, RAG, security, and session evals. Each template carries the judge prompt, the labels it returns, the score for each label, its optimization direction, and the granularity it evaluates at. Creating an evaluator from a template. Pick a template, then call POST /v2/evaluators with its fields mapped onto the request: | Template field | Where it goes in POST /v2/evaluators | | --- | --- | | column_name | version.template_config.name | | template | version.template_config.template | | classification_choices | version.template_config.classification_choices | | direction | version.template_config.direction | | data_granularity | version.template_config.data_granularity. Send SPAN, or omit it, when the template\u0026#x27;s value is null | | display_name | a label for your own use; reuse it for the evaluator\u0026#x27;s name or description | | rails | no destination; classification_choices already carries the same labels | Then add the fields a template doesn\u0026#x27;t carry: space_id, name, and type: TEMPLATE on the evaluator; a version.commit_message; and the execution settings template_config.include_explanations, use_function_calling_if_available, and llm_config. Finally, create a task to run the evaluator. A complete request built from the hallucination template: json { \u0026quot;space_id\u0026quot;: \u0026quot;U3BhY2U6NDkzOkJaSkc=\u0026quot;, \u0026quot;name\u0026quot;: \u0026quot;hallucination\u0026quot;, \u0026quot;description\u0026quot;: \u0026quot;Built from the hallucination template\u0026quot;, \u0026quot;type\u0026quot;: \u0026quot;TEMPLATE\u0026quot;, \u0026quot;version\u0026quot;: { \u0026quot;commit_message\u0026quot;: \u0026quot;Initial version from built-in template\u0026quot;, \u0026quot;template_config\u0026quot;: { \u0026quot;name\u0026quot;: \u0026quot;hallucination\u0026quot;, \u0026quot;template\u0026quot;: \u0026quot;You are evaluating whether an answer is factual given reference text...\\n{input}\\n{output}\u0026quot;, \u0026quot;classification_choices\u0026quot;: { \u0026quot;hallucinated\u0026quot;: 1, \u0026quot;factual\u0026quot;: 0 }, \u0026quot;direction\u0026quot;: \u0026quot;MINIMIZE\u0026quot;, \u0026quot;data_granularity\u0026quot;: \u0026quot;SPAN\u0026quot;, \u0026quot;include_explanations\u0026quot;: true, \u0026quot;use_function_calling_if_available\u0026quot;: true, \u0026quot;llm_config\u0026quot;: { \u0026quot;ai_integration_id\u0026quot;: \u0026quot;TGxtSW50ZWdyYXRpb246MTI6YUJjRA==\u0026quot;, \u0026quot;model_name\u0026quot;: \u0026quot;gpt-4o\u0026quot;, \u0026quot;invocation_parameters\u0026quot;: { \u0026quot;temperature\u0026quot;: 0 }, \u0026quot;provider_parameters\u0026quot;: {} } } } } Scope: this returns only the built-in catalog, which is identical for every caller and contains no space, account, or user data. It does not include the evaluators that already exist in your space. List those with GET /v2/evaluators. Pagination: not paginated. The catalog is a small fixed list (28 templates, roughly 32 KB of JSON) and the full set is always returned, so there is no cursor or limit. This endpoint is in alpha, read more here.\">\u003cmeta data-mint-head name=\"twitter:image\" content=\"https://arize-ax.mintlify.site/docs/_mintlify/api/og/v2?division=Evaluators\u0026amp;title=List+evaluator+templates\u0026amp;description=Retrieve+the+built-in+LLM-as-a-judge+evaluator+templates.+This+is+the+same%0Acatalog+the+product+offers+when+creating+an+evaluator%2C+spanning+response%0Aquality%2C+cod\u0026amp;theme=6b2e1c1a14346acb1262754f\u0026amp;signature=39ab18ef1f3644c8f1ca82e66028cf140de34c710807f6f7438b5a3ffb88dc91\">\u003cmeta data-mint-head name=\"twitter:image:width\" content=\"1200\">\u003cmeta data-mint-head name=\"twitter:image:height\" content=\"630\">\u003cmeta data-mint-head name=\"msapplication-config\" content=\"/docs/mintlify-assets/_mintlify/favicons/arize-ax/vxzD83QsbAzzm43O/_generated/favicon/browserconfig.xml\">\u003cmeta data-mint-head name=\"apple-mobile-web-app-title\" content=\"Arize AX Docs\">\u003cmeta data-mint-head name=\"msapplication-TileColor\" content=\"#7A0047\">\u003clink data-mint-head rel=\"canonical\" href=\"https://arize.com/docs/api-reference/evaluators/list-evaluator-templates\">\u003clink data-mint-head rel=\"alternate\" type=\"application/xml\" href=\"/docs/sitemap.xml\">\u003clink data-mint-head rel=\"alternate\" type=\"text/markdown\" href=\"/docs/api-reference/evaluators/list-evaluator-templates.md\">\u003cscript data-mint-head type=\"application/ld+json\">{\"@context\":\"https://schema.org\",\"@graph\":[{\"@type\":\"Organization\",\"@id\":\"https://arize.com/#organization\",\"name\":\"Arize AX Docs\",\"url\":\"https://arize.com\",\"logo\":{\"@type\":\"ImageObject\",\"url\":\"https://mintcdn.com/arize-ax/V-uvingrGLOCK4VD/logo/light.svg?fit=max\u0026auto=format\u0026n=V-uvingrGLOCK4VD\u0026q=85\u0026s=cc58ee9388d5b5253d39b65cc662908f\"}},{\"@type\":\"WebSite\",\"@id\":\"https://arize.com/docs#website\",\"name\":\"Arize AX Docs\",\"url\":\"https://arize.com/docs\",\"publisher\":{\"@id\":\"https://arize.com/#organization\"}},{\"@type\":\"WebPage\",\"@id\":\"https://arize.com/docs/api-reference/evaluators/list-evaluator-templates#webpage\",\"url\":\"https://arize.com/docs/api-reference/evaluators/list-evaluator-templates\",\"name\":\"List evaluator templates\",\"description\":\"Retrieve the built-in LLM-as-a-judge evaluator templates. This is the same catalog the product offers when creating an evaluator, spanning response quality, code quality, trajectory, RAG, security, and session evals. Each template carries the judge prompt, the labels it returns, the score for each label, its optimization direction, and the granularity it evaluates at. Creating an evaluator from a template. Pick a template, then call POST /v2/evaluators with its fields mapped onto the request: | Template field | Where it goes in POST /v2/evaluators | | --- | --- | | column_name | version.template_config.name | | template | version.template_config.template | | classification_choices | version.template_config.classification_choices | | direction | version.template_config.direction | | data_granularity | version.template_config.data_granularity. Send SPAN, or omit it, when the template's value is null | | display_name | a label for your own use; reuse it for the evaluator's name or description | | rails | no destination; classification_choices already carries the same labels | Then add the fields a template doesn't carry: space_id, name, and type: TEMPLATE on the evaluator; a version.commit_message; and the execution settings template_config.include_explanations, use_function_calling_if_available, and llm_config. Finally, create a task to run the evaluator. A complete request built from the hallucination template: json { \\\"space_id\\\": \\\"U3BhY2U6NDkzOkJaSkc=\\\", \\\"name\\\": \\\"hallucination\\\", \\\"description\\\": \\\"Built from the hallucination template\\\", \\\"type\\\": \\\"TEMPLATE\\\", \\\"version\\\": { \\\"commit_message\\\": \\\"Initial version from built-in template\\\", \\\"template_config\\\": { \\\"name\\\": \\\"hallucination\\\", \\\"template\\\": \\\"You are evaluating whether an answer is factual given reference text...\\\\n{input}\\\\n{output}\\\", \\\"classification_choices\\\": { \\\"hallucinated\\\": 1, \\\"factual\\\": 0 }, \\\"direction\\\": \\\"MINIMIZE\\\", \\\"data_granularity\\\": \\\"SPAN\\\", \\\"include_explanations\\\": true, \\\"use_function_calling_if_available\\\": true, \\\"llm_config\\\": { \\\"ai_integration_id\\\": \\\"TGxtSW50ZWdyYXRpb246MTI6YUJjRA==\\\", \\\"model_name\\\": \\\"gpt-4o\\\", \\\"invocation_parameters\\\": { \\\"temperature\\\": 0 }, \\\"provider_parameters\\\": {} } } } } Scope: this returns only the built-in catalog, which is identical for every caller and contains no space, account, or user data. It does not include the evaluators that already exist in your space. List those with GET /v2/evaluators. Pagination: not paginated. The catalog is a small fixed list (28 templates, roughly 32 KB of JSON) and the full set is always returned, so there is no cursor or limit. This endpoint is in alpha, read more here.\",\"dateModified\":\"2026-11-09T20:06:27.411Z\",\"isPartOf\":{\"@id\":\"https://arize.com/docs#website\"},\"breadcrumb\":{\"@id\":\"https://arize.com/docs/api-reference/evaluators/list-evaluator-templates#breadcrumb\"}},{\"@type\":\"BreadcrumbList\",\"@id\":\"https://arize.com/docs/api-reference/evaluators/list-evaluator-templates#breadcrumb\",\"itemListElement\":[{\"@type\":\"ListItem\",\"position\":1,\"name\":\"API reference\",\"item\":\"https://arize.com/docs/ax/rest-reference/overview\"},{\"@type\":\"ListItem\",\"position\":2,\"name\":\"REST\",\"item\":\"https://arize.com/docs/ax/rest-reference/overview\"},{\"@type\":\"ListItem\",\"position\":3,\"name\":\"Endpoints\",\"item\":\"https://arize.com/docs/api-reference/ai-integrations/list-ai-integrations\"},{\"@type\":\"ListItem\",\"position\":4,\"name\":\"List evaluator templates\",\"item\":\"https://arize.com/docs/api-reference/evaluators/list-evaluator-templates\"}]},{\"@type\":[\"TechArticle\",\"APIReference\"],\"@id\":\"https://arize.com/docs/api-reference/evaluators/list-evaluator-templates#apireference\",\"headline\":\"List evaluator templates\",\"name\":\"List evaluator templates\",\"description\":\"Retrieve the built-in LLM-as-a-judge evaluator templates. This is the same catalog the product offers when creating an evaluator, spanning response quality, code quality, trajectory, RAG, security, and session evals. Each template carries the judge prompt, the labels it returns, the score for each label, its optimization direction, and the granularity it evaluates at. Creating an evaluator from a template. Pick a template, then call POST /v2/evaluators with its fields mapped onto the request: | Template field | Where it goes in POST /v2/evaluators | | --- | --- | | column_name | version.template_config.name | | template | version.template_config.template | | classification_choices | version.template_config.classification_choices | | direction | version.template_config.direction | | data_granularity | version.template_config.data_granularity. Send SPAN, or omit it, when the template's value is null | | display_name | a label for your own use; reuse it for the evaluator's name or description | | rails | no destination; classification_choices already carries the same labels | Then add the fields a template doesn't carry: space_id, name, and type: TEMPLATE on the evaluator; a version.commit_message; and the execution settings template_config.include_explanations, use_function_calling_if_available, and llm_config. Finally, create a task to run the evaluator. A complete request built from the hallucination template: json { \\\"space_id\\\": \\\"U3BhY2U6NDkzOkJaSkc=\\\", \\\"name\\\": \\\"hallucination\\\", \\\"description\\\": \\\"Built from the hallucination template\\\", \\\"type\\\": \\\"TEMPLATE\\\", \\\"version\\\": { \\\"commit_message\\\": \\\"Initial version from built-in template\\\", \\\"template_config\\\": { \\\"name\\\": \\\"hallucination\\\", \\\"template\\\": \\\"You are evaluating whether an answer is factual given reference text...\\\\n{input}\\\\n{output}\\\", \\\"classification_choices\\\": { \\\"hallucinated\\\": 1, \\\"factual\\\": 0 }, \\\"direction\\\": \\\"MINIMIZE\\\", \\\"data_granularity\\\": \\\"SPAN\\\", \\\"include_explanations\\\": true, \\\"use_function_calling_if_available\\\": true, \\\"llm_config\\\": { \\\"ai_integration_id\\\": \\\"TGxtSW50ZWdyYXRpb246MTI6YUJjRA==\\\", \\\"model_name\\\": \\\"gpt-4o\\\", \\\"invocation_parameters\\\": { \\\"temperature\\\": 0 }, \\\"provider_parameters\\\": {} } } } } Scope: this returns only the built-in catalog, which is identical for every caller and contains no space, account, or user data. 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