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Why task endpoints with a fixed JSON schema are easier to ship and maintain than parsing free-text answers from a raw LLM prompt.
Each endpoint returns documented fields such as sentiment and confidence, validated before the response is sen...
Reliability & Contracts
{
"blueprint_slug": "structured-json-vs-raw-prompts",
"architecture_pattern": "Structured Endpoints",
"verdict": "Use structured endpoints for any result your code acts on. Keep raw prompts for open-ended text shown directly to users.",
"metrics": {
"Parsing": "JSON fields",
"Schema": "Documented",
"Model Changes": "Absorbed"
},
"key_capabilities": [
"No free-text parsing",
"Stable across model upgrades",
"Easy to store and query"
]
}Use structured endpoints for any result your code acts on. Keep raw prompts for open-ended text shown directly to users.
Each endpoint returns documented fields such as sentiment and confidence, validated before the response is sent.
You ask a general model for an answer and parse whatever text it returns.
Models sometimes add explanations, wrap JSON in markdown, rename keys or return a label you did not expect. Each of these becomes a bug in production. Structured endpoints validate and normalise the output so your code only sees the documented values.
When no task endpoint fits, the JSON Extractor endpoint accepts your own schema and returns data that matches it, plus a schema_conforms flag.
success, request_id, data.result and meta (version and timestamp). Errors use the same envelope with an error code and message.
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