Guardrails

Validates LLM-generated outputs against predefined schemas and constraints to ensure they conform to expected formats and data types. Catches malformed responses before they reach downstream systems.

Identifies when LLMs generate false or unsupported claims and automatically corrects them using provided context or knowledge sources. Reduces factual errors in AI-generated content.

Optimizes validation and correction processes to minimize token usage and API costs. Intelligently decides when to validate, correct, or accept outputs based on cost-benefit analysis.

Chains multiple validators together in sequence to apply layered validation rules to LLM outputs. Each validator can build on the results of previous validators for complex validation workflows.

Enforces specific output formats (JSON, XML, CSV, etc.) on LLM responses and automatically corrects formatting errors. Ensures outputs are machine-readable and compatible with downstream systems.

Validates LLM outputs against compliance rules, content policies, and regulatory requirements. Prevents generation of prohibited content and ensures adherence to organizational standards.

Integrates guardrails with multiple LLM providers and frameworks without requiring major application refactoring. Works as a middleware layer between application and LLM calls.

Provides framework and tools for building custom validators tailored to specific validation needs. Allows developers to extend validation capabilities beyond pre-built validators.

Detects validation failures and automatically re-prompts the LLM with corrective instructions to fix the output. Iteratively improves outputs without manual intervention.

Tracks and logs all LLM outputs, validation results, and corrections for audit trails and debugging. Provides visibility into validation pipeline behavior and failure patterns.

Validates LLM outputs for semantic correctness and coherence using context from the conversation or domain knowledge. Goes beyond format validation to check meaning and relevance.