You're Not Using Enough Guardrails — Here's What Actually Works (1787906985667)

Everyone talks about AI guardrails. Most of them check the wrong thing.

Input guardrails vs output guardrails

Most guardrail solutions (content filters, prompt injection detectors, topic classifiers) operate on the input — what the user asks. They prevent bad prompts from reaching the model.

But the real cost comes from output problems — the model confidently returning wrong answers that pass all input filters.

What output guardrails should catch

1. Fabricated citations — the model makes up sources
2. Hallucinated tool arguments — wrong parameters passed to APIs
3. System prompt leakage — internal instructions leaked in output
4. Safety refusal bypass — the model says "I can't answer" but then answers anyway
5. Code with logic errors — syntactically valid but semantically wrong

A different approach

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

The key insight: post-generation verification catches things pre-generation filters miss, because you're checking the actual output, not guessing what the model might produce.

Download: https://agent-download-site.vercel.app

Free, model-agnostic, CPU-only.