The output schema defines what your endpoint returns. While optional, defining an output schema ensures consistent responses and enables validation of LLM outputs.
Why Define Output Schema?
Consistent Responses Ensure every response follows the same structure
Documentation OpenAPI docs show exactly what callers receive
Validation Catch malformed LLM responses before they reach your app
Type Safety Frontend/backend code can rely on specific fields
Adding Output Fields Navigate to your endpoint’s Output Schema tab and click Add Field .
Field Configuration Property Required Description Name Yes Field name in the response JSON Type Yes Data type (string, number, boolean, array, object) Description No What this field contains
Output fields are never “required” in the traditional sense—they define what the LLM should return. If the LLM doesn’t include a field, it will be omitted from the response.
Field Types Output schemas support the same types as input schemas:
String
Number
Boolean
Array
Object
{ "name" : "summary" , "type" : "string" , "description" : "The summarized content" }
{ "name" : "confidence" , "type" : "number" , "description" : "Confidence score between 0 and 1" }
{ "name" : "is_spam" , "type" : "boolean" , "description" : "Whether the content is spam" }
{ "name" : "keywords" , "type" : "array" , "items" : { "type" : "string" }, "description" : "Extracted keywords" }
{ "name" : "metadata" , "type" : "object" , "properties" : { "word_count" : { "type" : "integer" }, "language" : { "type" : "string" } } }
Prompting for Structured Output Your prompt must instruct the LLM to return JSON matching your schema. Here’s the pattern:
Analyze the following text for sentiment: {{ inputs . text }} Respond with a JSON object containing: - sentiment: "positive", "negative", or "neutral" - confidence: a number between 0 and 1 - keywords: an array of key terms that influenced the analysis Return only valid JSON, no additional text.
If your prompt doesn’t ask for JSON output, the LLM may return plain text that won’t parse correctly.
Example Schemas Sentiment Analysis Output Schema: { "type" : "object" , "properties" : { "sentiment" : { "type" : "string" , "description" : "Overall sentiment" }, "confidence" : { "type" : "number" , "description" : "Confidence score 0-1" }, "aspects" : { "type" : "array" , "items" : { "type" : "object" , "properties" : { "aspect" : { "type" : "string" }, "sentiment" : { "type" : "string" } } }, "description" : "Sentiment by aspect" } } }
Example Response: { "sentiment" : "positive" , "confidence" : 0.92 , "aspects" : [ { "aspect" : "price" , "sentiment" : "neutral" }, { "aspect" : "quality" , "sentiment" : "positive" }, { "aspect" : "delivery" , "sentiment" : "positive" } ] }
Content Classification Output Schema: { "type" : "object" , "properties" : { "category" : { "type" : "string" , "description" : "Primary category" }, "subcategories" : { "type" : "array" , "items" : { "type" : "string" }, "description" : "Secondary categories" }, "confidence_scores" : { "type" : "object" , "description" : "Confidence per category" } } }
Output Schema: { "type" : "object" , "properties" : { "entities" : { "type" : "array" , "items" : { "type" : "object" , "properties" : { "text" : { "type" : "string" }, "type" : { "type" : "string" }, "start" : { "type" : "integer" }, "end" : { "type" : "integer" } } } }, "summary" : { "type" : "string" } } }
Response Validation When an output schema is defined, Endprompt:
Parses the LLM response as JSON
Validates against your schema
Returns the validated response
If validation fails, you’ll see details in the execution logs.
Start without an output schema while experimenting, then add one once you’ve stabilized the prompt’s output format.
JSON Schema Preview The Output Schema tab shows the complete JSON Schema:
{ "type" : "object" , "properties" : { "summary" : { "type" : "string" , "description" : "Concise summary of the content" }, "bullet_points" : { "type" : "array" , "items" : { "type" : "string" }, "description" : "Key points as bullet list" }, "word_count" : { "type" : "integer" , "description" : "Number of words in summary" } } }
Best Practices
Flat structures are easier for LLMs to produce consistently than deeply nested ones.
Tell the LLM exactly what JSON structure you expect, including field names.
Use descriptive field names
Field names like is_spam are clearer than spam or s.
Add descriptions to every field—they appear in your OpenAPI docs.
Test with unusual inputs to see if the LLM still produces valid JSON.
Without Output Schema If you don’t define an output schema:
The raw LLM response is returned
No JSON parsing or validation occurs
The response may be plain text or unstructured
This is fine for simple use cases where you just need text back.
Next Steps 
