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

说明与用途

后生成验证层，检测并纠正LLM输出中的错误，如伪造引用、幻觉工具参数、系统提示泄露、安全拒绝绕过和代码逻辑错误。

问题

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.

结果

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

工作流

• 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.

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

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

来源