Safety and Refusal Behaviors

Claude is designed with Constitutional AI, meaning it has built-in safety guardrails. In a RAG application, you might encounter "False Refusals"—where the model refuses to answer a legitimate question because it incorrectly flags the context as dangerous or sensitive.

Common Reasons for Refusal

1. Copyrighted Material: If your RAG system contains full chapters of books or protected code, Claude may refuse to summarize them.
2. PII (Personally Identifiable Information): Claude is cautious about handling data that looks like passwords, social security numbers, or medical records.
3. Medical/Legal Advice: It may refuse to give a definitive "Yes/No" on high-stakes topics even if the context contains the answer.

Tuning for RAG

To minimize false refusals:

• Be Explicit: Tell Claude exactly what role it is playing.
  • "You are a corporate legal assistant summarizing internal contracts. This data is authorized for this use."
• Handle 'I don't know': Provide a safe fallback so the model doesn't feel forced to answer if it's unsure.

Dealing with "Hidden" Knowledge

Sometimes Claude will refuse because it "knows" something is controversial from its training data, even if your context is neutral. Strategy: Use high-temperature settings or a more permissive system prompt (within safety limits).

Monitoring Refusals

If you are using AWS Bedrock, you can monitor the guardrail metrics to see how often queries are being blocked before they even reach the model.

Ethical Alignment

Always ensure your RAG system follows the Rule of Three:

1. Is it Helpful?
2. Is it Honest?
3. Is it Harmless?

Exercises

1. Give Claude a piece of context about a "fake" medical drug. Does it refuse to answer questions about dosage?
2. How would you prompt the model to distinguish between "General Information" and "Medical Advice"?
3. What is the difference between an "LLM Refusal" and a "Retrieval Failure"?