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

Description and use

A post‑generation verification layer that detects and corrects LLM output errors such as fabricated citations, hallucinated tool arguments, system prompt leakage, safety refusal bypass, and code logic errors.

Problem

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

Outcome

The post-generation guardrail layer catches output problems that pre-generation filters miss, automatically correcting many issues and reducing the need for human intervention.

Workflow

• 1. Receive user prompt and send to LLM.
• 2. Generate output from the model.
• 3. Run the output through 13 detectors grouped into 5 categories (fabricated citations, hallucinated tool arguments, system prompt leakage, safety refusal bypass, code logic errors).
• 4. Apply 31 correction strategies to automatically fix detected issues.
• 5. If a 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

If a correction is ambiguous, it flags the output for human review.

Source