'structured output' 검색 결과

Comprehensive patterns for AI-powered document understanding including PDF parsing, OCR, invoice/receipt extraction, table extraction, multimodal RAG with vision models, and structured data output. Use when "document parsing, PDF extraction, OCR, invoice processing, receipt extraction, document understanding, LlamaParse, Unstructured, vision document, table extraction, structured output from PDF, " mentioned.

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Expert guide for agent-native structured outputs, JSON Schema strict mode enforcement (OpenAI response_format, Gemini schema mode, Claude tool_use), MCP resource schemas, and zero-hallucination type contracts / Panduan ahli kontrak output terstruktur native agen dan penegakan JSON schema ketat.

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Design, create, inspect, update, attach, detach, preview, execute, and verify reusable Vapi Structured Outputs through public API or Server SDK workflows. Use for post-call extraction, typed call artifacts, AI-versus-regex extraction, JSON Schema design, backfilling existing calls, or retrieving structured results programmatically.

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Evaluates and scores Claude prompts and system prompts using a six-dimension Scorecard with anchored 1-5 rubrics. Trigger on: "review my prompt," "score this prompt," "rate my prompt," "evaluate this prompt," "improve my prompt," "what's wrong with my prompt." Also trigger on: "why isn't my prompt working," "my prompt produces bad output," "help me fix this prompt," "is this prompt any good." Scorecard covers Objective Clarity, Context Specificity, Reasoning Fit, Output Precision, Behavioral Calibration, and Architectural Efficiency, plus a five-question Output Evaluation Rubric for assessing actual output. Also use when the user pastes a system prompt and asks for feedback. Activate whenever structured quality assessment of a single prompt is the primary need. Do NOT use for project-level audits involving Custom Instructions architecture, knowledge file organization, or multi-file Project structure (use rootnode-project-audit if available).

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Implements input and output validation guardrails for LLM-powered applications to prevent prompt injection, data leakage, toxic content generation, and hallucinated outputs. Builds a security validation pipeline using NVIDIA NeMo Guardrails Colang definitions, custom Python validators for PII Tespit and content policy enforcement, and the Guardrails AI framework for structured output validation. The guardrails system intercepts both user inputs (blocking injection attempts, stripping PII, ...

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Extract structured data from LLM responses with Pydantic validation, retry failed extractions automatically, parse complex JSON with type safety, and stream partial results with Instructor - battle-tested structured output library

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Extract structured data from LLM responses with Pydantic validation, retry failed extractions automatically, parse complex JSON with type safety, and stream partial results with Instructor - battle-tested structured output library

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Extracts structured data from LLM responses using JSON schemas, Zod validation, and function calling for reliable parsing. Use when users request "structured output", "JSON extraction", "parse LLM response", "function calling", or "typed responses".

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Plan market, competitor, consumer, or financial research with chapter outlines, data requirements, and chart plans. Then turn collected evidence into a structured Markdown report with comparisons, citations, and strategic implications, while clearly identifying missing data.

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• Planning research chapters and data collection requirements
• Turning market and competitor findings into a report
• Organizing consumer insights and brand strategy analysis

Audit breadcrumb structured data, a machine-readable description of a page’s navigation path. Check that BreadcrumbList matches the visible trail and site hierarchy, then guide JSON-LD changes using the rendered HTML and HTTP response.

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• Check breadcrumb structured data
• Add JSON-LD for visible breadcrumbs
• Review site hierarchy after template changes

Delegate a clearly scoped repository analysis or implementation task to Pi without an interactive session. The skill limits the working directory and available tools, then checks structured output for errors before using the result.

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• Delegate a read-only code diagnosis to Pi
• Assign Pi an explicitly authorized implementation task
• Check Pi's structured output for model or tool failures

Delegate a clearly scoped coding task to Qoder in a specified project without opening its interactive interface. Read the structured JSON response and check its error flag before accepting the result.

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• Ask Qoder to review a specific file without editing
• Delegate a clearly scoped coding task to Qoder
• Check errors in Qoder's structured response

Turn JSON or CSV data into Markdown notes using Knap templates. Apply formatting rules and conditional sections, generate files in batches, or shape web content extracted by Defuddle into a consistent note format.

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• Generate Markdown notes from JSON or CSV data
• Create multiple notes with a shared template
• Format Defuddle output into a note

Design and refine prompts for AI applications using examples, reusable templates, and structured outputs such as JSON. Test variations for accuracy, consistency, response time, and token usage to diagnose unreliable answers and formatting failures.

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• Refine prompts that produce inconsistent answers
• Build reusable prompt templates with variables
• Design JSON outputs that follow a defined schema

Organize the outputs of a substantial work session into linked knowledge pages with sources. A seven-phase review checks classification rules, identifies reusable workflows, audits overlapping skills, and verifies that saved pages and skills can be found.

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• Saving the findings from a completed research session
• Organizing outputs from a development session
• Finding workflows worth turning into reusable skills

Extract fields, filter records, and reshape JSON from API responses, configuration files, or logs using Crush’s built-in jq. Get help writing and debugging transformations, constructing new JSON, and formatting output for other commands.

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• Extract selected fields from API responses
• Filter JSON arrays by conditions
• Reshape configuration data or construct JSON

Test marketing prompts against shared cases, score required content, prohibited phrases, and output format, and compare alternatives. Keep version history and diffs, use reusable templates, and check that prompt or model updates preserve output quality.

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• Compare two prompts for ads, email, or social content
• Keep prompt versions and review changes
• Check output quality after prompt or model updates

Improve prompts through test cases and baseline comparisons, measure how well AI answers use retrieved sources, and check agent configurations. Includes Python tools for prompt analysis, retrieval quality reports, and token estimates, with costs calculated from supplied rates.

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• Compare prompt revisions against a baseline
• Measure answer quality against retrieved sources
• Check agent tool settings and loop risks

Build conversations and workflows with the Gemini Interactions API on Gemini Enterprise Agent Platform. Follow Python or JavaScript examples for server-stored conversation history, streaming responses, structured JSON output, function calls, and background agent tasks.

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• Building chats that retain conversation context
• Displaying responses as they stream
• Returning JSON in a specified format

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

Plan projects that use large language models, from checking whether the task suits a model to designing processing stages. Estimate costs, choose single or multiple agents, and organize prototypes, structured outputs, and iteration.

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• Checking whether a task is suitable for an LLM
• Designing a pipeline for batch analysis
• Estimating processing costs before development

Choose and operate KrillinAI CLI commands for subtitles, dubbing, video rendering, covers, and speech. This skill guides setup and helps interpret structured responses, output records, generated files, and errors.

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• Choose commands for subtitles, dubbing, and video output
• Build and configure the CLI
• Check generated files and diagnose errors

Turn papers, code, experiment logs, and notes into an ARA, a structured research package for AI agents. Organizes claims, evidence, verification plans, code stubs, and research decisions, then checks coverage and consistency across the generated files.

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• Organize a paper and its code into a research package
• Link research claims to tables, figures, and verification plans
• Preserve failed approaches and research decisions

Fine-tune language models with TRL using GRPO, a method that compares scored responses to the same prompt. Covers training data, scoring functions, GPU-conscious settings, and monitoring for tasks with measurable correctness or required output formats.

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• Train models to follow JSON or XML output formats
• Reward correct answers in math or coding tasks
• Balance correctness, format, and style during training