Insurance Coverage Interpretation Guardrail Prompt

This prompt is a guardrail, not a product feature. It is designed for insurance AI systems that handle policy-related questions from policyholders, agents, or internal staff. The core job-to-be-done is to create a reliable, auditable refusal layer that prevents the model from crossing into binding insurance decisions—such as making coverage determinations, adjudicating claims, or deciding benefit eligibility—while still permitting the model to explain policy language, define terms, and summarize general plan provisions. The ideal user is a product engineer or compliance architect who needs to ship an AI feature that can discuss insurance documents without creating regulatory liability or customer confusion about what the AI is authorized to decide.

Deploy this prompt when your application must draw a bright line between explaining a policy and interpreting it in a way that binds the carrier. For example, a policyholder asking 'Does my plan cover physical therapy?' should receive a summary of the relevant policy section and general definitions, not a 'yes' or 'no' that could be construed as a coverage determination. The prompt enforces this by instructing the model to refuse direct answers to coverage questions and to instead offer to locate and explain the relevant policy language. It is appropriate for customer-facing chatbots, agent support tools, and internal knowledge bases where the AI sits upstream of a licensed adjuster or authorized representative.

Do not use this prompt for generating policy documents, comparing plans for sales purposes, providing financial advice, or any workflow where the model is expected to make a definitive recommendation. It is also insufficient as a standalone compliance control; you must pair it with application-layer validation that scans outputs for binding determination language (e.g., 'you are covered,' 'this claim will be paid,' 'you are eligible for benefits') and routes flagged responses for human review. In regulated insurance contexts, this prompt is one layer in a defense-in-depth strategy that includes logging, human-in-the-loop escalation, and regular eval testing against a golden dataset of coverage questions with known acceptable and unacceptable response patterns.

This guardrail prompt is designed to be placed as a system instruction or as a pre-processing layer before any user-facing response in an insurance AI feature. Its primary job is to draw a hard boundary: the model must refuse to make coverage determinations, claim adjudications, or benefit eligibility decisions, while still being permitted to explain policy language, define terms, and summarize factual provisions. The square-bracket placeholders allow you to inject your specific policy language, product context, and escalation instructions without rewriting the core refusal logic.

textCopy
You are an insurance information assistant. Your role is to help users understand insurance policy language, definitions, and general concepts. You are not a licensed insurance agent, adjuster, or underwriter.

[POLICY_DEFINITIONS]
- Coverage determination: Any statement about whether a specific loss, event, or circumstance is covered under a policy.
- Claim adjudication: Any statement about whether a claim should be paid, denied, or partially paid.
- Benefit eligibility: Any statement about whether a person qualifies for benefits under a policy.

[RULES]
1. You MUST refuse any request that asks you to make a coverage determination, claim adjudication, or benefit eligibility decision.
2. You MAY explain policy language, define insurance terms, summarize policy provisions, and describe how coverage types generally work.
3. You MAY provide factual information about deductibles, limits, exclusions, and conditions as written in the policy.
4. You MUST NOT use language that implies a binding determination, such as "you are covered," "this claim will be paid," "you qualify for benefits," or "this loss is excluded."
5. When refusing, use the refusal template below. Do not elaborate on why the specific situation might or might not be covered.

[REFUSAL_TEMPLATE]
"I can't make coverage determinations or tell you whether a specific situation is covered. Coverage depends on the full policy terms, the specific facts of a loss, and applicable law. I can help you understand what your policy says about [TOPIC] or explain general insurance concepts. For a coverage determination, please contact your agent, adjuster, or the claims department at [ESCALATION_CONTACT]."

[SAFE_ALTERNATIVES]
When you refuse, offer one of these alternatives if relevant:
- Explain a specific policy provision the user is asking about.
- Define an insurance term the user is unfamiliar with.
- Summarize what a section of the policy generally addresses.
- Direct the user to [ESCALATION_CONTACT] for binding answers.

[INPUT]
[USER_QUERY]

[OUTPUT]
Provide your response following the rules above. If the request requires refusal, use the refusal template. If the request is for policy explanation, provide the explanation without making any coverage determination.

To adapt this template for your product, replace the bracketed placeholders with your organization's specific language. [POLICY_DEFINITIONS] should reflect your legal team's exact definitions of prohibited determination types. [ESCALATION_CONTACT] should route users to the correct human channel—an agent, adjuster, or claims department. [USER_QUERY] is where your application injects the user's actual message. The [SAFE_ALTERNATIVES] section should be customized to match the permitted capabilities of your specific AI feature. Before deploying, run this prompt through your eval suite using test cases that probe the boundary between explanation and determination—phrasings like "am I covered if..." and "would my policy pay for..." should consistently trigger the refusal template, while "what does this exclusion mean" should return an explanation without a refusal.

This prompt is designed to sit behind a customer-facing or internal insurance AI feature that receives natural-language questions about policy coverage, benefits, or claims. The implementation harness must treat every model response as a candidate that requires validation before it reaches the user. Because the prompt's core job is to refuse binding determinations while permitting policy language explanation, the harness should enforce a hard post-processing gate: if the response contains any language that could be interpreted as a coverage determination, claim adjudication, or benefit eligibility decision, it must be blocked or routed to human review regardless of the model's surface-level compliance.

Wire the prompt as a synchronous API call with a structured output contract. Require the model to return a JSON object with at minimum three fields: response_type (enum: explanation, refusal, clarification), content (the user-facing text), and flags (array of detected risk signals such as coverage_determination_risk, eligibility_statement_risk, claim_adjudication_risk). On the application side, implement a validator that checks response_type against the expected safe set for the user's context. If response_type is refusal, allow the response through but log the interaction for compliance review. If response_type is explanation, run a secondary regex and keyword scan for forbidden phrases such as 'you are covered,' 'your claim is approved,' 'you qualify for,' 'this is covered under your plan,' or 'your benefit amount is.' Any match triggers either a retry with a stricter system instruction or immediate escalation to a human review queue. For high-risk deployments, add a third validation layer using a separate classifier model fine-tuned on coverage-determination boundary cases to score the output before release.

Retry logic should be bounded. If the primary model returns a flagged response, retry once with an amplified instruction that prepends 'CRITICAL: You must not make any coverage determination, eligibility statement, or claim adjudication. If you cannot answer without doing so, respond only with a refusal and explanation of why.' If the retry also fails validation, do not attempt a third generation. Instead, log the full interaction trace—including the original user query, both model responses, and the validator results—and route to a human review queue with a clear summary of the escalation reason. This prevents infinite retry loops on adversarial or ambiguous inputs while maintaining an audit trail for compliance and product improvement. For applications with high throughput, consider caching validated refusal templates for common boundary-crossing query patterns to reduce latency and model cost on repeated near-identical requests.

Model choice matters here. Use a model with strong instruction-following and low refusal drift, such as Claude 3.5 Sonnet or GPT-4o, rather than smaller or less steerable models that may comply with the letter of the prompt while violating its intent under pressure. If you must use a smaller model for cost or latency reasons, invest in a robust output validator and expect higher retry and escalation rates. Do not use this prompt with models that lack reliable system-instruction adherence, as the liability surface is too high. For RAG-augmented deployments where policy documents are retrieved and provided as context, ensure the retrieved chunks are scoped to definitions and general provisions—never include coverage-specific or claim-adjudication sections in the context window, as the model may inadvertently synthesize a determination from them.

Logging and observability are non-negotiable. Every interaction must be logged with: the user query, the retrieved context (if any), the full prompt sent to the model, the raw model response, the validator results, the final displayed response (or escalation action), and a session identifier. This audit trail serves compliance, product improvement, and incident response. Set up monitoring alerts on the rate of coverage_determination_risk flags and escalation events. A sudden spike may indicate a prompt regression, a model behavior change, or an adversarial probing campaign. Finally, schedule quarterly reviews of logged escalations with your compliance or legal team to identify patterns that require prompt updates, additional refusal templates, or product-scope adjustments.