You're Not Using Enough Guardrails — Here's What Actually Works

Description and use

A post-generation verification layer detects and corrects output errors missed by pre-generation filters, enhancing AI safety and accuracy.

Problem

AI guardrails often focus on input filtering, missing costly output issues like fabricated citations, hallucinated tool arguments, system prompt leakage, safety refusal bypass, and code logic errors.

Outcome

The post-generation guardrail system effectively catches and corrects output problems that pre-generation filters miss, improving reliability and safety of AI-generated content.

Workflow

• 1. Receive user prompt and send to LLM.
• 2. Generate output from the model.
• 3. Run the post-generation guardrail layer on the output.
• 4. Apply one of 31 correction strategies if a detector flags an issue.
• 5. If correction is ambiguous, flag the output for human review.
• 6. Deliver the corrected or reviewed output to the user.

Evidence

Instead of filtering before generation, I built a layer that filters after generation. It uses 13 detectors across 5 categories, with 31 correction strategies that fix issues automatically.

Source