'structured generation' 검색 결과

Build Python workflows that constrain language model output using text patterns and grammar rules. Generate structured JSON, restrict classifications to allowed labels, extract information, and combine reusable generation steps with loops and tool selection.

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• Generate JSON with a defined structure
• Constrain date, email, and identifier formats
• Classify text using a fixed set of labels

Build Python workflows that constrain language model output using text patterns and grammar rules. Generate structured JSON, restrict classifications to allowed labels, extract information, and combine reusable generation steps with loops and tool selection.

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• Generate JSON with a defined structure
• Constrain date, email, and identifier formats
• Classify text using a fixed set of labels

Maximum control over AI image generation — write structured VGL (Visual Generation Language) JSON that explicitly controls every visual attribute. Define exact object placement, lighting direction, camera angle, lens focal length, composition, color scheme, and artistic style as deterministic JSON instead of ambiguous natural language. Use this skill when you need reproducible image generation, precise control over scene composition, or want to convert a natural language image request into a structured JSON schema for Bria FIBO models. Triggers on requests for structured prompts, controllable generation, VGL JSON, deterministic image descriptions, or Bria/FIBO structured_prompt format.

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Use Outlines to make language models produce data in a defined format. The skill covers extracting information into JSON, restricting classification labels, and applying Pydantic models, which define fields and data types, with local model backends.

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• Extracting company or profile details into JSON
• Classifying text using a fixed set of labels
• Generating text that matches a specified pattern

Use Outlines to make language models produce data in a defined format. The skill covers extracting information into JSON, restricting classification labels, and applying Pydantic models, which define fields and data types, with local model backends.

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• Extracting company or profile details into JSON
• Classifying text using a fixed set of labels
• Generating text that matches a specified pattern

Create character, scene, and product images from structured descriptions of style, composition, lighting, and color. Guide generation with reference images and aspect ratio settings, using a Python script connected to a supported image generation service.

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• Creating characters with specified clothing, era, and appearance
• Visualizing scenes with a chosen mood, weather, and lighting
• Producing product images with specified materials and viewing angles

Build prompts for FLUX image generation and editing, with guidance on model selection, lighting, composition, text, and precise colors. It also covers structured scene descriptions using JSON and editing with multiple reference images.

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• Writing prompts for image generation
• Specifying brand colors and text
• Editing with multiple reference images

Comprehensive patterns for building AI-powered code generation tools, code assistants, automated refactoring, code review, and structured output generation using LLMs with function calling and tool use. Use when "code generation, AI code assistant, function calling, structured output, code review AI, automated refactoring, tool use, code completion, agent code, " mentioned.

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Guarantee valid JSON/XML/code structure during generation, use Pydantic models for type-safe outputs, support local models (Transformers, vLLM), and maximize inference speed with Outlines - dottxt.ai's structured generation library

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Add on-device language model features to iOS 26+ apps with Apple's FoundationModels framework. Covers text generation, summarization, extracting data into Swift types, calling custom app functions, and progressively updating screens as responses are generated.

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• Adding offline text generation and summarization
• Extracting structured data from user input
• Connecting AI responses to custom app functions

Turn document analysis into a structured PPT presentation with images generated in a chosen style. The skill uses the NanoBanana stack to combine document-based content preparation and image generation within a presentation creation workflow.

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• Turning a document into a PPT presentation
• Creating slides with styled, generated images
• Combining document analysis and presentation creation

Generate slides, documents, websites, and diagrams from natural language prompts through AnyGen OpenAPI. Manage reference uploads, check generation progress, and download finished files using commands with structured output for AI agents.

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• Generate slides or documents from written instructions
• Upload reference files for a generation task
• Check generation progress and download finished files

Set up LLM serving with structured responses, including JSON and outputs restricted to specified patterns. Covers reusing shared prompt calculations, tool calling, and multi-turn conversations through SGLang, with examples for server setup and application integration.

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• Extract information into JSON with a defined schema
• Build agents that call tools
• Reuse shared prompts across chat requests

Helps build AI features in Go with Genkit, including text generation, structured data output, streaming, tool calls, and conversational agents. Guides HTTP serving and trace inspection to check model responses and tool execution.

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• Adding text generation or structured output to a Go app
• Building a chatbot that retains conversation state
• Connecting model responses to tool calls

Build LLM serving workflows that produce JSON or other required formats. The skill covers reusing computation for shared prompts, tool calls, and ongoing conversations, with examples for server setup and structured output.

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• Extracting article details into JSON
• Building agents that call tools
• Reusing shared prompts across conversations

Audit website structured data, the markup that describes businesses and content to machines. Detect errors and missing details, then generate JSON-LD code and a report with recommendations for improving AI readability.

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• Audit structured data on a company website
• Generate markup for products, articles, or local businesses
• Review links connecting official profiles

Use this skill when writing code that calls the Gemini API for text generation, multi-turn chat, multimodal understanding, image generation, video generation, speech generation (TTS), voice design, voice replication, streaming responses, background research tasks, function calling, structured output, or migrating from the old generateContent API. Covers SDK usage and best practices for Gemini models and agents in Python and TypeScript.

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Control LLM output with regex and grammars, guarantee valid JSON/XML/code generation, enforce structured formats, and build multi-step workflows with Guidance - Microsoft Research's constrained generation framework

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Control LLM output with regex and grammars, guarantee valid JSON/XML/code generation, enforce structured formats, and build multi-step workflows with Guidance - Microsoft Research's constrained generation framework

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Generate model responses in defined formats for data extraction and classification. Use JSON schemas, Pydantic data models, regular expressions, or fixed choices to produce structured results that other programs can parse and validate.

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• Extracting company or product details from text
• Classifying reviews into predefined labels
• Turning profile text into structured fields

Generate videos from structured prompts describing subjects, visual style, camera work, and other scene details. The workflow creates English JSON prompts and runs a Python script, with optional reference images to guide the result.

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• Turn a scene idea into a video
• Generate a video guided by a reference image
• Specify visual style and camera movement

Design workflows that evaluate and refine AI outputs through self-review, separate generation and evaluation roles, tests, or weighted scoring criteria. Includes patterns for structured feedback, iteration limits, and stopping when quality stops improving.

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• Adding self-review and revision loops to AI outputs
• Separating generation from evaluation
• Refining generated code using test results

Turn image briefs into structured prompts for GPT-Image-2 using reusable JSON templates, organized fields for visual requirements. Specify composition, text, lighting, and constraints for product images, posters, infographics, and other design work.

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• Prepare prompts for product images
• Specify poster titles and layouts
• Structure infographic content

Invokes Google Gemini models for structured outputs, image generation, text-to-speech narration, multi-modal tasks, and Google-specific features. Use when users request Gemini, image generation, Gemini TTS or a synthesized voice, structured JSON output, Google API integration, or cost-effective parallel processing.

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