tutorial

Eliminate Manual Data Parsing with Gemini's Structured Outputs

Q: What prompt should I use?

from pydantic import BaseModel\nfrom google import genai\n\nclass [DATA_STRUCTURE_NAME](BaseModel):\n [FIELD_NAME]: [TYPE]\n\nclient = genai.Client()\nresponse = client.models.generate_content(\n model='gemini-1.5-flash',\n contents='[USER_PROMPT]',\n config={'response_mime_type': 'application/json', 'response_schema': [DATA_STRUCTURE_NAME]}\n)

Published 06:03 MYT · Jun 22

Force Gemini to return type-safe JSON using Pydantic or Zod schemas.

Use the Gemini API's Structured Output feature to define a schema that the model must follow, ensuring error-free data integration.

Google Gemini

The Scenario

You are building an app that extracts nutritional facts from food photos or recipes and needs to save that data directly into a database without format errors.

Before & after

The old way

Developers manually wrote regex or custom parsers to clean '```json' tags and fix trailing commas from raw LLM responses, taking 15–30 minutes per integration.

With AI

Define a Pydantic class and pass it to the Gemini client. The model generates valid JSON automatically in under 1 minute.

The Prompt

from pydantic import BaseModel\nfrom google import genai\n\nclass [DATA_STRUCTURE_NAME](BaseModel):\n    [FIELD_NAME]: [TYPE]\n\nclient = genai.Client()\nresponse = client.models.generate_content(\n    model='gemini-1.5-flash',\n    contents='[USER_PROMPT]',\n    config={'response_mime_type': 'application/json', 'response_schema': [DATA_STRUCTURE_NAME]}\n)

Using the Google GenAI SDK, you can define your schema in native Python (Pydantic) or JavaScript (Zod). This forces the model to adhere to your data structure, eliminating the need for 'prompt engineering' to fix formatting issues.

Source

Release notes | Gemini API | Google AI for Developers

"generate responses that map directly to your app's internal data structures, saving you from writing complex parsers or brittle error-handling logic."