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Reliably extract structured data — JSON, tables, lists — from AI responses.
Read through the lesson, mark it complete when the concept is clear, then move to the next lesson in the sequence or jump back to the module map.
Unstructured AI output is unusable in automated pipelines. To build reliable systems, you need predictable output formats that can be parsed and processed downstream.
The good news: modern models are excellent at following format instructions when you give them explicitly.
Extract the following information from the text and return it as valid JSON.
Schema:
{
"company": string,
"revenue": number (in millions),
"employees": number,
"founded": number (year)
}
Text: "{paste text}"
Return only the JSON object. No explanation.
The "Return only the JSON object. No explanation." instruction is critical — without it, models wrap JSON in prose.
For human-readable structured output, specify markdown components:
Analyze the provided code. Format your response as:
## Summary
One paragraph overview.
## Issues Found
Bulleted list. Each item: [SEVERITY] Description
## Recommendations
Numbered list ordered by priority.
For complex extraction, chain multiple prompts:
This is more reliable than trying to do everything in one prompt.
Always validate AI-generated structured output before using it. Use Zod or similar schema validation:
const schema = z.object({
company: z.string(),
revenue: z.number(),
})
const validated = schema.parse(JSON.parse(aiOutput))
Never trust unvalidated AI output in production systems.