I need a tool that can alert on hallucinations and unsafe content

Yes — you can build or buy a LLM output guardrail tool that flags:

• Hallucinations: claims not supported by source data
• Unsafe content: self-harm, violence, hate, sexual content, illegal instructions, PII leakage, etc.

Common ways to do it

1. Output moderation

   • Run every model response through a safety classifier.
   • Good for detecting policy-violating content.

2. Groundedness / factuality checks

   • Compare the response against trusted sources, retrieval context, or knowledge base.
   • Flag unsupported statements, fabricated citations, or inconsistent answers.

3. PII / secrets detection

   • Scan outputs for personal data, API keys, credentials, or sensitive business info.

4. Rule-based policy layer

   • Use regexes and deterministic rules for high-risk patterns.
   • Useful as a fast first pass.

5. LLM-as-judge

   • A second model reviews the answer and labels issues.
   • Flexible, but should be combined with deterministic checks.

What a practical setup looks like

• Input filters: detect unsafe user prompts before generation
• Generation-time controls: constrain retrieval, citations, and tool use
• Post-generation checks: evaluate output for:
  • safety
  • factual grounding
  • policy compliance
  • PII/secrets
• Escalation: block, redact, or send for human review

Off-the-shelf options

Depending on your stack, you can look at:

• OpenAI moderation / safety tooling
• Azure AI Content Safety
• AWS Bedrock Guardrails
• Google Vertex AI safety features
• Custom validators with LangChain, LlamaIndex, Guardrails AI, or Presidio

If you want to build it yourself

A solid minimal architecture is:

• Classifier for safety
• Retriever-backed groundedness checker
• PII detector
• Policy engine
• Logging + review dashboard

Important caveat

No detector will be perfect. The best systems:

• combine multiple checks
• tune thresholds by risk level
• allow human review for ambiguous cases

If you want, I can help you with one of these next:

1. Recommend a specific product stack
2. Design an architecture
3. Write a Python prototype
4. Draft evaluation metrics for hallucination/unsafe-content detection