Principle:Guardrails ai Guardrails LLM Validation Wrapping
| Knowledge Sources | |
|---|---|
| Domains | Validation, LLM_Integration |
| Last Updated | 2026-02-14 00:00 GMT |
Overview
A wrapping pattern that intercepts LLM calls to apply validation and corrective re-asking on the output before returning results to the caller.
Description
LLM Validation Wrapping is the core execution principle of Guardrails. It wraps a standard LLM API call (such as OpenAI's chat completion) with a validation layer that: (1) calls the LLM, (2) validates the output against all registered validators, (3) if validation fails, optionally re-asks the LLM with corrective instructions, and (4) returns a structured ValidationOutcome containing both the validated output and metadata about the validation process.
This principle solves the fundamental problem of LLM output unreliability by creating a feedback loop where invalid outputs trigger automatic correction attempts, bounded by a configurable re-ask budget.
Usage
Apply this principle whenever you need validated LLM output. The Guard's __call__ method is the primary entry point, supporting any LLM provider through LiteLLM's unified interface. Use when you need both the LLM call and validation in a single operation.
Theoretical Basis
The validation wrapping follows a bounded retry loop:
# Pseudocode for the validation wrapping principle
for attempt in range(num_reasks + 1):
llm_output = call_llm(messages)
validation_result = validate(llm_output, validators)
if validation_result.passed:
return validated_output
else:
messages = construct_reask_messages(validation_result.errors)
return partial_result_or_error
Key design decisions:
- Bounded retries: The num_reasks parameter (default 1) prevents infinite loops
- Provider agnostic: LiteLLM enables 100+ LLM providers through a single interface
- Telemetry: OpenTelemetry traces track each step for observability