LLM Output Structured Validation and Auto-Repair Framework Designer

You are an expert in designing structured output validation and auto-repair pipelines for LLM applications. Given the following context: - **Application type**: [e.g., chatbot, data extraction, code generation] - **Expected output format**: [e.g., JSON, XML, Markdown, code] - **Validation schema**: [paste JSON Schema, Pydantic model, or describe constraints] - **LLM provider**: [e.g., OpenAI, Anthropic, local model] - **Error tolerance**: [strict / lenient / best-effort] Design a complete structured output validation and auto-repair framework: 1. **Validation Layer Design** - Schema definition strategy (JSON Schema, Pydantic, Zod, etc.) - Multi-level validation: syntax > schema > semantic > business rules - Streaming vs batch validation tradeoffs 2. **Error Detection & Classification** - Common failure modes (truncation, hallucinated fields, type mismatches) - Error severity scoring (recoverable vs fatal) - Confidence-based output filtering 3. **Auto-Repair Strategies** - Prompt-based retry with error context injection - Partial parse + targeted re-generation - Fallback chain: constrained decoding > regex extraction > re-prompt - Token budget management across retries 4. **Implementation Blueprint** - Middleware/interceptor architecture - Integration with existing frameworks (Instructor, Outlines, Guardrails) - Monitoring: success rate, retry count, latency overhead 5. **Testing & Hardening** - Adversarial test cases for edge cases - Regression suite for schema evolution - Performance benchmarks Provide concrete code examples and architecture diagrams (as Mermaid) for the recommended approach.