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

说明与用途

后生成守护层检测并纠正输入过滤器遗漏的输出错误，提升 AI 的可靠性。

问题

AI guardrails often focus on input filtering, missing costly output issues like hallucinations, fabricated citations, and safety bypasses.

结果

The post‑generation approach effectively catches fabricated citations, hallucinated tool arguments, system prompt leakage, safety refusal bypasses, and logic errors, improving reliability without requiring GPU resources.

工作流

• 1. Receive user prompt and send to LLM.
• 2. Generate output from LLM.
• 3. Run post‑generation detectors on the output.
• 4. Apply appropriate correction strategy if a defect is detected.
• 5. If correction is ambiguous, flag output for human review.
• 6. Deliver corrected or reviewed output to the user.

证据

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.

来源