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ChatGPTMidjourneyClaude
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Define data structure clearly
Specify JSON format, CSV columns, or data schemas
Mention specific libraries
PyTorch, TensorFlow, Scikit-learn for targeted solutions
Clarify theory vs. production
Specify if you need concepts or deployment-ready code
Get structured data from LLMs with Instructor. Pattern: 1. Define Pydantic models for output. 2. Use instructor.patch() on OpenAI client. 3. LLM returns validated objects. 4. Automatic retry on validation errors. 5. Partial streaming for progressive updates. 6. Union types for multiple formats. 7. Nested models for complex data. 8. Field descriptions guide LLM. Type-safe LLM outputs. Use for data extraction and classification.
A proven free prompt for Instructor structured LLM outputs is: "Get structured data from LLMs with Instructor. Pattern: 1. Define Pydantic models for output. 2. Use instructor.patch() on OpenAI client. 3. LLM returns validated objects. 4. Automatic retry on valida..." — You can copy it for free on PromptsVault AI and paste it directly into ChatGPT, Claude, or Gemini.
Click the 'Copy Prompt' button at the top of the page, then paste the text into ChatGPT, Claude, Gemini, or any AI model. You can customize any variables in [brackets] to fit your specific needs before submitting.
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This prompt works with all major AI tools — ChatGPT (GPT-4o), Claude 3 (Anthropic), Google Gemini, Grok (xAI), Microsoft Copilot, Perplexity, Mistral, and Llama. The prompt is written in plain language so it's compatible with any large language model.