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 input filters, improving AI reliability.

Problem

AI guardrails that filter only inputs miss costly output problems such as fabricated citations, hallucinated tool arguments, system prompt leakage, safety refusal bypass, and code with logic errors.

Outcome

The post‑generation approach catches issues that pre‑generation filters miss, reducing errors and improving trust in AI outputs.

Workflow

• 1. Receive user prompt and generate model output.
• 2. Run the output through the 13‑detector verification layer.
• 3. Apply the appropriate correction strategy (e.g., remove fabricated citations, correct tool arguments).
• 4. If correction is ambiguous, flag the output for human review.
• 5. Deliver the corrected or reviewed output to the user.

Evidence

Most guardrail solutions (content filters, prompt injection detectors, topic classifiers) operate on the input — what the user asks.

Source

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