Prompt Engineering

Can an insurer convert general-purpose AI capability into repeatable, controlled assistance for claims, underwriting, operations, and management without allowing the model to invent facts, exceed authority, or obscure uncertainty? Prompt engineering is one part of that answer. The other parts are trusted data, retrieval, workflow controls, evaluation, and professional review.

Updated: July 2026 • 12 min read

Foundational12 min readInsurance executives, claims leaders, underwriters, technology and data leaders

A Practical Guide for Property & Casualty Insurance

Insurance AI Dictionary & Reference GuideAI Fundamentals

The Business Question

Executive Snapshot

Dimension	What Leaders Should Know
Primary purpose	Translate a business task and operating policy into clear instructions an AI system can follow.
Best use	Repeatable analysis, summarization, extraction, drafting, classification, research support, and workflow assistance.
Not sufficient alone	Prompts do not create authoritative knowledge, eliminate hallucinations, or replace access controls and validation.
Key control	Require source evidence, bounded scope, structured output, uncertainty disclosure, and human review for consequential work.
Success measure	Accuracy and usefulness in the real workflow—not how impressive a single demonstration appears.
Executive posture	Treat prompts as governed production assets, not informal personal notes.

Five Leadership Takeaways

Definitions and Quick Facts

A shared vocabulary for business, technology, risk, and insurance teams.

Write prompts with the same care as procedures or decision rules.
Prompt quality depends on task clarity, evidence, context, and constraints.
Govern reusable prompts through ownership, testing, version control, and change management.

Executive Definition

Prompt engineering is the design and governance of instructions that shape how an AI model interprets a task and produces an output.

Technical Definition

Prompt engineering is the systematic construction and evaluation of system messages, user instructions, examples, retrieved context, tool specifications, output schemas, and safety constraints supplied to a language or multimodal model at inference time.

Insurance Definition

In P&C insurance, prompt engineering converts claims, underwriting, policy, operational, and management objectives into bounded AI tasks that preserve source traceability, role authority, confidentiality, and human accountability.

Quick Facts

Question	Practical Answer
Is a prompt code?	Not usually, but a production prompt behaves like a configurable software and policy asset.
Does a longer prompt work better?	Not necessarily. Clarity, hierarchy, relevant context, and test coverage matter more than length.
Can prompts stop hallucinations?	They can reduce risk, but grounding, validation, tools, and review are also required.
Should prompts include examples?	Often. Examples clarify expected reasoning and format, but can also introduce bias or overfitting.
Who should own prompts?	Named business and technical owners, with risk functions involved as appropriate.

IMPORTANT DISTINCTION

Prompt engineering guides inference-time behavior; fine-tuning changes model parameters; retrieval supplies approved context. They are complementary, not interchangeable.

DESIGN

The Anatomy of an Effective Insurance Prompt

A practical structure for turning an insurance task into controlled model behavior.

Component	Purpose	Insurance Example
Role	Defines perspective and boundaries.	Act as a claims-analysis assistant; do not make coverage or settlement decisions.
Objective	States the task and intended outcome.	Summarize new medical developments and identify changes relevant to severity review.
Context	Provides workflow, claim, account, or policy background.	The audience is a senior casualty examiner reviewing an open bodily-injury claim.
Evidence	Specifies approved sources and citation expectations.	Use only the attached claim notes and documents; cite document name and page.
Constraints	Prohibits unsupported or unauthorized behavior.	Do not infer diagnoses, negligence, fraud, or legal conclusions.
Method	Defines required analytical steps.	Separate observed facts, calculated values, and professional-review questions.
Output schema	Makes the result consistent and machine-usable.	Return JSON with event_date, event_type, source, confidence, and review_flag.
Uncertainty rule	Requires disclosure of gaps and ambiguity.	State “not supported by the provided record” when evidence is absent.
Review instruction	Preserves human authority.	Flag recommendations for adjuster review; do not execute claim actions.

A Reusable Prompt Blueprint

PROMPT BLUEPRINT

ROLE → BUSINESS OBJECTIVE → APPROVED CONTEXT → SOURCE RULES → TASK STEPS → CONSTRAINTS → OUTPUT FORMAT → UNCERTAINTY HANDLING → HUMAN REVIEW

DESIGN

Instruction Hierarchy and Prompt Layers

Understanding where behavior is defined in an enterprise AI application.

Layer	Typical Owner	Function	Example
System instruction	Platform / product owner	Defines durable identity, policies, safety, and boundaries.	Never provide a final coverage determination.
Developer or workflow instruction	Application team	Defines task logic, tools, schemas, and process rules.	Retrieve policy forms before answering a coverage research question.
User instruction	Authorized professional	Provides the immediate business request.	Summarize treatment changes since the last reserve review.
Retrieved context	Knowledge / data layer	Supplies source material.	Claim notes, medical documents, policy forms, underwriting guidelines.
Tool response	Approved enterprise service	Returns calculations or system facts.	Loss payment total, policy dates, geocoded property attributes.
Output validator	Application / control layer	Checks format, evidence, prohibited content, and policy compliance.	Reject an answer missing citations or required review flags.

Conflict Resolution

Production applications should define which instructions outrank others. A user should not be able to override system-level confidentiality rules, approved tool boundaries, or human-authorization requirements. Retrieved documents should be treated as evidence, not as instructions, unless explicitly designated otherwise.

CONTROL PRINCIPLE

Keep policy and authority instructions at a higher layer than ad hoc user requests. Treat untrusted document text as data—even when it contains imperative language.

TECHNIQUES

Core Prompting Techniques

Methods are useful only when selected for the task and evaluated in context.

Technique	Description	Best Insurance Use	Primary Caution
Zero-shot	Instruction without examples.	Simple extraction, classification, or summarization with clear schemas.	Ambiguity may produce inconsistent interpretations.
One-shot	One example demonstrates expected behavior.	Standard claim-note summary or referral format.	The model may mimic accidental features of the example.
Few-shot	Several examples define a pattern.	Complex labels, nuanced document routing, or tone consistency.	Examples may encode historical bias or incomplete policy.
Decomposition	Breaks a task into smaller stages.	Timeline → evidence gaps → review questions → summary.	Errors can propagate between stages.
Self-check instruction	Asks the model to verify constraints or evidence.	Confirm every finding has an approved source citation.	Not a substitute for independent validation.
Structured output	Requires fields, tables, or JSON.	Workflow integration and automated quality checks.	Schema compliance does not guarantee factual correctness.
Tool-use prompting	Defines when and how to call approved tools.	Calculations, policy retrieval, system lookups.	Excessive permissions can create operational risk.

Reasoning Instructions

For enterprise insurance workflows, prompts should request concise, reviewable reasoning artifacts such as evidence tables, assumptions, calculations, decision factors, and unresolved questions. Applications should not depend on exposing hidden internal model reasoning. The goal is an auditable work product, not a transcript of every internal token-level step.

PATTERNS

High-Value Prompt Patterns for P&C Insurance

Reusable patterns that support evidence, consistency, and professional review.

EVIDENCE-FIRST SUMMARY

Extract supported facts with source locations before writing the narrative summary.

CHANGE DETECTION

Compare current and prior records; report only material additions, removals, or contradictions.

ISSUE SPOTTING

Identify potential review topics without making legal, medical, fraud, or coverage conclusions.

TIMELINE NORMALIZATION

Convert unstructured events into dated entries with source and confidence.

GAP ANALYSIS

List required data elements, what is present, what is missing, and why the gap matters.

REFERRAL PREPARATION

Organize evidence and questions for a human specialist without recommending an unauthorized outcome.

POLICY RESEARCH

Retrieve controlling forms and quote or paraphrase relevant provisions with citations and version context.

PORTFOLIO NARRATIVE

Translate approved metrics into an executive summary while distinguishing observation from interpretation.

APPLICATIONS

Prompt Engineering Across the Claims Lifecycle

Prompts should assist claims professionals while preserving authority and documentation standards.

Claims Stage	Prompt-Enabled Assistance	Required Guardrail
FNOL	Summarize loss facts, parties, injuries, property damage, and missing intake data.	Do not determine liability or coverage.
Assignment / triage	Prepare a routing rationale using approved rules and model signals.	Final assignment follows operational authority.
Investigation	Create timelines, identify contradictions, and organize evidence requests.	Separate allegation, observation, and inference.
Medical review	Summarize treatment, procedures, work status, and changes over time.	No diagnosis or medical-necessity conclusion unless performed by an authorized professional.
Reserve review	Describe material developments and evidence relevant to exposure.	No autonomous reserve change.
Litigation monitoring	Summarize pleadings, discovery, counsel updates, and deadlines.	No legal advice or strategy determination.
Settlement preparation	Organize damages, negotiations, authority, and unresolved issues.	No autonomous offer, acceptance, or release action.
Closure	Check file documentation and unresolved tasks against approved criteria.	Human confirms closure and compliance.

Claims Design Principle

A claims prompt should produce a reviewable evidence package, not a hidden decision. The adjuster or claims leader should be able to see the sources, understand uncertainty, correct the output, and document the final professional judgment.

CASE STUDY

Casualty Claim: New Medical Developments

A prompt-driven workflow for monitoring severity without delegating claim authority.

Business Situation

A bodily-injury claim has accumulated treatment notes, imaging reports, attorney correspondence, and reserve-review comments. The claim professional needs to understand what changed since the prior review and whether the file requires closer attention.

Prompt Design

Use only documents received after the prior review date plus the prior review summary.
Create a dated evidence table before drafting conclusions.
Identify new procedures, diagnoses reported in the record, work-status changes, specialist referrals, attorney activity, and contradictions.
Distinguish explicit documentation from interpretation.
State missing or unreadable information.
Prepare review questions; do not recommend a reserve amount, liability position, or settlement action.

Illustrative Output Structure

Section	Expected Content
Material changes	Concise list of developments supported by citations.
Timeline	Date, event, source, page, and relevance.
Potential severity factors	Factors for professional review—not a conclusion.
Contradictions / gaps	Conflicting dates, unclear causation, missing reports, or incomplete records.
Review questions	Questions for adjuster, nurse reviewer, counsel, or supervisor.
Uncertainty statement	What cannot be established from the supplied record.

BUSINESS VALUE

The workflow reduces document-review time and improves consistency while leaving reserve, liability, medical, and settlement judgments with authorized professionals.

APPLICATIONS

Prompt Engineering Across Underwriting

The objective is faster, more consistent risk understanding—not automatic risk selection without authority.

Underwriting Activity	Prompt-Enabled Assistance	Required Guardrail
Submission intake	Summarize operations, locations, exposures, limits, loss history, and missing items.	Preserve source provenance and avoid unsupported assumptions.
Appetite review	Map known facts to approved appetite criteria.	Flag ambiguity; do not invent a match.
Risk research	Organize public and licensed data into a review package.	Respect permitted-use and privacy restrictions.
Loss-run review	Normalize claims, identify patterns, and reconcile totals.	Validate calculations and data completeness.
Referral preparation	Draft the issue summary, evidence, and questions for senior authority.	Do not bypass authority thresholds.
Quote communication	Draft clear rationale and information requests.	Underwriter approves terms and customer-facing language.
Renewal review	Compare current and prior account information and changes.	Use correct policy period and versioned guidelines.

Underwriting Design Principle

Prompts should make the submission easier to understand, expose information gaps, and prepare a disciplined decision record. They should not conceal the origin of facts or collapse underwriting judgment into a generic model response.

CASE STUDY

Commercial Underwriting Submission Review

Using prompts to prepare an evidence-backed risk brief.

Business Situation

A middle-market submission contains an application, supplemental questionnaires, five years of loss runs, inspection photographs, financial statements, and broker correspondence. Information conflicts across documents, and several required fields are incomplete.

Prompt Workflow

Classify and inventory all submission documents.

Extract core account facts with source citations.

Normalize locations, payroll, sales, vehicles, limits, deductibles, and loss totals.

Identify contradictions and missing requirements.

Compare only against the approved appetite and referral guide retrieved for the relevant product and effective date.

Produce a risk brief and referral questions; do not bind, decline, price, or set terms.

Quality Controls

Control	Purpose
Document-level provenance	Allows the underwriter to verify every material fact.
Period validation	Prevents mixing prior and current policy-year information.
Arithmetic verification	Recalculates totals through an approved tool rather than model estimation.
Guideline version check	Confirms that appetite and authority rules are current and applicable.
Conflict surfacing	Prevents the model from silently choosing among inconsistent sources.
Underwriter approval	Preserves authority over risk selection, price, terms, and communication.

ARCHITECTURE

Prompts, Retrieval, Tools, and Structured Data

Prompt engineering is strongest when it orchestrates trusted capabilities instead of asking the model to know everything.

Capability	What It Contributes	Example
Prompt	Task definition, rules, constraints, and format.	Summarize material changes and cite sources.
Retrieval	Relevant unstructured evidence.	Claim notes, policy forms, guidelines, medical documents.
Structured data	Authoritative fields and measures.	Policy dates, paid amounts, reserves, vehicle schedule.
Tools	Deterministic actions or calculations.	Date math, totals, database lookup, geospatial service.
Validator	Checks output against schemas and policies.	Citation completeness, prohibited conclusions, required fields.
Workflow	Routes the result to the right professional and records action.	Supervisor referral or underwriting review queue.

Prompt Engineering within RAG

The prompt should tell the model how to use retrieved evidence, what to do when sources conflict, how to cite sources, and when to stop rather than speculate. Retrieval quality and access controls are separate responsibilities; a well-written prompt cannot compensate for irrelevant or unauthorized context.

Prompt Engineering for AI Agents

Agent prompts define goals, tool permissions, stopping conditions, escalation rules, and review points. In insurance, agentic designs should use least privilege, explicit transaction boundaries, and approval gates before any consequential system action.

ENGINEERING

Structured Outputs and Workflow Integration

Reliable automation requires outputs that systems and professionals can consistently interpret.

Why Structured Output Matters

Free-form prose may be useful for reading, but production workflows often need predictable fields. Structured output allows validation, routing, comparison, metrics, and safe downstream use. It also makes prompt failures easier to detect.

Field	Example Requirement	Validation Rule
finding_type	medical_change, legal_event, coverage_question	Must match approved enumeration.
event_date	2026-07-15	Must be a valid date or explicitly unknown.
finding_text	Concise supported observation.	No prohibited conclusion or unsupported statement.
source_reference	Document name, page, section.	Must resolve to accessible evidence.
confidence	high, medium, low	Must reflect evidence quality, not model certainty alone.
review_required	true / false	True for specified high-risk categories.
review_reason	Why professional review is needed.	Required when review_required is true.

DESIGN RULE

Validate both syntax and meaning. A perfectly formatted JSON response can still contain incorrect, incomplete, or unauthorized content.

EVALUATION

Testing Prompt Quality

Production readiness requires repeatable evaluation against representative insurance work—not anecdotal demonstrations.

Evaluation Dimension	Illustrative Measure	Why It Matters
Factual accuracy	Supported findings / total findings	Measures whether statements are grounded.
Citation accuracy	Correct and resolvable citations / citations	Tests traceability.
Completeness	Required facts captured / known required facts	Detects omissions.
Instruction adherence	Required rules followed / evaluated rules	Tests constraint compliance.
Abstention quality	Correctly declines when evidence is insufficient	Reduces speculation.
Format compliance	Schema-valid outputs / outputs	Supports workflow integration.
Professional usefulness	Reviewer rating and correction effort	Measures actual business value.
Fairness / consistency	Performance by relevant segment and scenario	Identifies uneven quality or bias.
Latency and cost	Time and cost per completed task	Tests operational feasibility.

Build a Prompt Test Set

GOVERNANCE

Prompts as Controlled Production Assets

Governance should be proportional to the consequence of the task and the authority of the workflow.

Representative routine cases
Difficult edge cases
Conflicting and incomplete records
Adversarial or malicious instructions
Sensitive-data scenarios
Different lines of business and jurisdictions
Known historical outcomes with expert-reviewed reference answers
Cases where the correct behavior is to abstain or escalate

Control	Production Expectation
Ownership	Named business owner and technical owner.
Purpose limitation	Documented approved use, users, data, and outputs.
Version control	Prompt text, model, retrieval settings, tools, and schemas tracked together.
Testing and approval	Predeployment evaluation with defined acceptance criteria.
Change management	Material changes trigger regression testing and review.
Access control	Only authorized users can view, edit, publish, or execute prompts.
Logging	Record prompt version, inputs, retrieved sources, outputs, actions, and reviewer disposition as permitted.
Monitoring	Track errors, drift, incidents, overrides, and user feedback.
Retirement	Remove obsolete prompts and dependent workflow configurations safely.

Risk Tiering

A prompt that rewrites internal meeting notes does not require the same controls as a prompt supporting claim severity review, underwriting referral, policy interpretation, or customer communication. Risk tiering should consider data sensitivity, decision consequence, degree of automation, external impact, and reversibility.

SECURITY

Prompt Injection, Data Leakage, and Misuse

Prompt security is an application and operating-model problem—not a phrase added to the prompt.

Threat	Example	Control Response
Direct prompt injection	User asks the system to ignore its governing rules.	Instruction hierarchy, authorization, validation, and refusal behavior.
Indirect prompt injection	A retrieved document contains instructions to reveal data or change behavior.	Treat retrieved content as untrusted data; isolate and sanitize.
Data leakage	Sensitive claim or policy information appears in an unauthorized output.	Least-privilege retrieval, access filters, redaction, and output controls.
Tool misuse	Agent is induced to call a system or perform an unauthorized action.	Scoped tools, parameter validation, approval gates, and transaction limits.
Context poisoning	Incorrect or malicious content is inserted into the knowledge source.	Source governance, provenance, validation, and monitoring.
Prompt exfiltration	User attempts to reveal confidential system instructions.	Do not expose protected instructions; monitor repeated attempts.
Denial / cost abuse	Excessive prompts or context create operational cost.	Rate limits, token budgets, quotas, and anomaly monitoring.

SECURITY PRINCIPLE

Never place secrets, passwords, API keys, or unnecessary personal information in prompts. Do not rely on the model to enforce permissions that should be implemented in the application and data layers.

RISK MANAGEMENT

Privacy, Legal, and Compliance Considerations

Prompt design must operate within the insurer’s broader data and decision-governance framework.

Data minimization

Include only information necessary for the approved task. Use masking, redaction, or references where appropriate.

Purpose and permitted use

Confirm that data sources, third-party content, and model services may be used for the intended insurance purpose.

Retention and logging

Define whether prompts and outputs are retained, for how long, and who may access them.

Regulated and consequential decisions

Require professional review and documentation when outputs may affect claim handling, underwriting, pricing, coverage, or customers.

Records and discoverability

Treat production prompts, outputs, and logs as potential business records subject to retention, litigation, and audit requirements.

Jurisdiction and policy version

Ensure the prompt uses applicable law, policy form, endorsement, guideline, and effective date rather than generic content.

Consumer communication

Use approved language, disclosures, accessibility standards, and review processes before external delivery.

OPERATING MODEL

Human-in-the-Loop Design

The professional should remain able to understand, challenge, correct, and own the result.

Consumer communication

Control Point	Human Responsibility	AI Responsibility
Task initiation	Select approved workflow and confirm purpose.	Apply the governed prompt and authorized context.
Evidence review	Verify material facts and source quality.	Organize evidence and expose citations.
Judgment	Make claim, underwriting, legal, medical, or management decisions within authority.	Present factors, patterns, gaps, and questions.
Action approval	Approve or reject consequential action.	Prepare drafts or recommendations without exceeding permissions.
Documentation	Record rationale and required file notes.	Provide structured support and audit metadata.
Feedback	Correct errors and report issues.	Capture approved feedback for evaluation and improvement.

Meaningful Review

A checkbox is not meaningful human oversight. Reviewers need adequate time, authority, source access, training, and clear accountability. The application should highlight uncertainty and material evidence rather than presenting an answer with unjustified confidence.

IMPLEMENTATION

A Practical Implementation Blueprint

Start with a bounded problem, prove quality in the workflow, and scale through governance.

1. DEFINE THE USE CASE Specify the user, business objective, current process, consequence, and prohibited outcomes.

2. MAP AUTHORITY AND RISK Document who may decide, approve, communicate, and act.

3. IDENTIFY APPROVED EVIDENCE Define structured and unstructured sources, access rules, and version requirements.

4. DESIGN THE PROMPT CONTRACT Write role, objective, constraints, method, schema, uncertainty, and review rules.

5. BUILD REPRESENTATIVE TESTS Include routine, edge, incomplete, conflicting, adversarial, and abstention cases.

6. CONFIGURE RETRIEVAL AND TOOLS Use least privilege and deterministic services where possible.

7. VALIDATE WITH PROFESSIONALS Measure quality, usefulness, correction effort, and risk.

8. PILOT IN A CONTROLLED WORKFLOW Limit users, monitor outputs, and maintain rollback capability.

9. GOVERN, MONITOR, AND IMPROVE Version the full configuration; track quality, overrides, incidents, adoption, cycle time, and feedback.

EXECUTION

Best Practices and Common Mistakes

The difference between a demo prompt and a dependable production asset.

Best Practices

Begin with the business decision or work product, not with a model feature.
Write instructions in clear, testable language and separate durable policy from task-specific input.
Use approved sources and require citations for material findings.
Ask for structured evidence and uncertainty, not polished prose alone.
Use tools for calculations and authoritative system facts.
Design explicit stop, abstain, and escalate behavior.
Test prompt changes against a fixed regression set.
Measure reviewer correction effort and business outcomes.
Keep humans in authority for consequential insurance decisions.

Common Mistakes

EXECUTIVE GUIDANCE

Executive Decision Guide

Questions to ask before funding or approving a prompt-driven insurance capability.

Using one giant prompt for multiple unrelated tasks.
Assuming prompt wording can compensate for poor data or retrieval.
Allowing users to override core safety or authority instructions.
Embedding outdated policy, appetite, or legal content directly in prompts without version control.
Treating confidence language as calibrated probability.
Publishing model-generated customer or legal communications without review.
Testing only successful examples selected by the project team.
Changing the model or retrieval configuration without regression testing.

Decision Question	Evidence of Readiness	Warning Sign
Is the task bounded?	Clear user, input, output, authority, and prohibited outcomes.	“Help with claims” or another undefined objective.
Are sources trustworthy?	Approved, accessible, versioned evidence with provenance.	Uncontrolled web or mixed confidential sources.
Can quality be measured?	Reference answers, acceptance thresholds, and review metrics.	Success defined by impressive anecdotes.
Is human authority explicit?	Named reviewer and approval gates for consequential work.	Model output flows directly into decisions or communications.
Are prompts governed?	Ownership, versioning, testing, logs, and change control.	Personal prompts copied informally across teams.
Is security engineered?	Least privilege, injection defenses, tool controls, and monitoring.	Security instruction exists only inside the prompt.
Can the workflow fail safely?	Abstention, escalation, rollback, and incident response.	The system always returns an answer.
Is value demonstrable?	Cycle time, quality, leakage, consistency, and user outcomes tracked.	Only token cost or usage volume is measured.

GO / NO-GO RULE

Do not deploy a prompt into a consequential workflow until the organization can explain what the system may do, what it may not do, what evidence it uses, how performance is tested, and who remains accountable.

PERSPECTIVE

The Charlee.ai Perspective

Prompt engineering should strengthen insurance judgment by making AI assistance evidence-based, explainable, and operationally controlled.

In P&C insurance, the valuable output is not a clever response. It is a dependable work product that helps a claims professional, underwriter, manager, or implementation team understand information earlier and act with greater confidence. Prompt engineering contributes by defining what the AI should examine, how it should organize evidence, what it must not infer, and where professional review is required.

People Plus AI

Charlee.ai’s responsible-AI philosophy centers on professional authority. AI can monitor large volumes of information, identify patterns, prepare timelines, summarize documents, and surface questions. Insurance professionals retain responsibility for interpreting evidence, applying policy and judgment, and making decisions.

Design Principles

Approved purpose and data
Validated AI analysis
Explanation and source evidence
Professional review
Decision and documentation
Monitoring, feedback, incident response, and controlled change

CORE MESSAGE

Prompt engineering is a control surface—not the entire control system. Responsible insurance AI combines governed prompts with reliable data, retrieval, validation, workflow integration, human expertise, and continuous monitoring.

EDUCATION

Common Myths and Practical Reality

Clear expectations support responsible adoption.

Myth	Practical Reality
“Prompt engineering is just clever wording.”	Production prompting is requirements engineering, workflow design, testing, and governance.
“A perfect prompt eliminates hallucinations.”	Risk reduction also requires grounding, tools, validation, and review.
“More detail always improves the answer.”	Irrelevant or conflicting detail can reduce quality and raise cost.
“The model understands our policies automatically.”	Policies must be supplied through approved, current, and correctly scoped context.
“Structured output means the content is correct.”	Structure enables validation but does not establish factual accuracy.
“Users can safely edit production prompts.”	Uncontrolled edits can change authority, evidence rules, and risk behavior.
“Human review fixes every problem.”	Review must be meaningful, supported, and designed into the workflow.
“Prompt libraries can be copied across carriers.”	Prompts must reflect each organization’s products, authority, data, procedures, and governance.

REFERENCE

Frequently Asked Questions — Part 1

Executive and operational questions about prompt engineering in insurance.

Prompt Engineering Control Stack

Show layered instructions: system policy → workflow instructions → user task → retrieved evidence and tools → structured output → validation and professional review. Use a clean enterprise architecture style.