What should I use to track hallucinations and unsafe outputs in my chatbot?

To track hallucinations and unsafe outputs in a chatbot, you generally want a combination of logging, evaluation, and monitoring tools rather than just one thing.

What to use

1. Conversation logging

Store:

• user prompts
• model responses
• retrieved sources/context
• model version, temperature, prompts, system messages
• timestamps, latency, token usage
• user feedback and moderation flags

This is the foundation for finding bad outputs later.

2. Automated safety filters / moderation

Use a classifier or moderation layer to detect:

• toxic language
• self-harm content
• harassment
• sexual content
• violent content
• policy-violating or dangerous instructions

Examples:

• OpenAI Moderation
• Azure AI Content Safety
• Google Perspective API for toxicity
• custom classifiers for domain-specific risks

3. Hallucination / factuality evaluation

Track whether the answer is grounded in allowed sources or retrieval context.

Useful approaches:

• RAG faithfulness checks
• citation verification
• answer-vs-context consistency scoring
• LLM-as-judge evals for factuality

Common tools/frameworks:

• Ragas
• TruLens
• DeepEval
• Arize Phoenix
• LangSmith evals
• Langfuse evals/observability

4. Human review workflow

Automatically flag:

• low-confidence responses
• safety-filter hits
• answers with missing citations
• user complaints
• high-impact domains like medical/legal/financial advice

Then send them to a review queue.

5. Monitoring dashboards

Track metrics over time:

• hallucination rate
• unsafe output rate
• refusal rate
• escalation rate
• user-reported bad answers
• retrieval failure rate
• confidence/grounding scores

Good practical stack

If you want a solid default setup:

• Langfuse or LangSmith for tracing and observability
• OpenAI Moderation or Azure AI Content Safety for safety filtering
• Ragas or TruLens for hallucination/factuality evals
• Human review queue for flagged conversations

Recommendation by use case

• Simple chatbot: logging + moderation + manual review
• RAG chatbot: logging + citation checks + RAG evals
• High-risk domain: logging + moderation + factuality evals + human review + strict policies

If you want, I can suggest a specific stack for your backend like Python, Node.js, OpenAI, LangChain, or LlamaIndex.