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To further AI understanding and adoption in the insurance industry, Lazarus AI is producing a series of articles titled Artificial Intelligence – Insights for Insurance (“AI-II”). Several of these insights will focus on prompting, as it is a foundational component of implementing enterprise-scale AI solutions.

This piece introduces key concepts in prompt engineering. Future articles will expand on these ideas in greater depth. The perspectives shared here are informed by Lazarus AI’s expertise in large language models (LLMs) and our experience deploying them in real-world environments.

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Prompt Engineering Defined

Prompt engineering is the practice of crafting instructions, commands, and questions to effectively utilize an LLM. 

Successful prompting is both an art and a science. The term “engineering” is intentional as engaging with an LLM is not an ad hoc interaction, but a designed process where the quality of outputs is directly tied to how inputs are constructed. 

In early 2023, the rapid expansion of LLM capabilities led to a widespread assumption that larger models would consistently produce better results. Over time, a different reality emerged: for many business applications, improved prompting (not larger or fine-tuned models) was the primary driver of better outcomes.

LLMs are powerful tools, but that potential cannot be fully realized without investing sufficient time and effort into learning how to interface with them. A useful analogy is a highly skilled engineer who does not speak your language. The limitation is not their intelligence, but the communication barrier that prevents you from accessing their skills. The solution is not to replace them with a more capable engineer, but to establish a clearer method of communication. In this context, better prompting is often more impactful than pursuing increasingly complex models.

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Evolution of Prompt Engineering in Insurance: 2024 and Beyond

Insurance organizations must begin developing prompt engineering capabilities now.

While insurers face a range of competing priorities (e.g. macroeconomic pressures, workforce shifts, and increasing claims complexity) delaying investment in prompting introduces a structural disadvantage in how AI is applied.

Leading technology companies, including Lazarus AI, have already invested in prompt engineering. However, prompting cannot be handled solely by technology partners. To fully utilize the power of LLMs, insurers must incorporate their own domain expertise into how prompts are designed and evaluated.

This capability will not remain specialized for long.

As LLMs become embedded in more business processes, the ability to engage with them effectively will become a baseline expectation for knowledge workers. The trajectory mirrors other foundational tools in the enterprise. Take the ubiquity of Google and spreadsheets in the modern corporation, for instance. No organization assigns ownership of search or spreadsheets to a single role. Instead, effective usage becomes distributed, with shared norms and expectations. Prompting will follow a similar path, but with that shift comes a need for practical rigor.

Users understand that search results reflect ranking mechanisms and bias. Spreadsheet users validate provenance before relying on outputs. Prompting will require the same level of scrutiny: understanding how outputs are generated, when they can be trusted, and where limitations exist. Soon, general prompting will become a core competency across the organization.

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The Evolving Role of the Prompt Engineer

This shift does not eliminate the need for prompt engineers. It redefines it.

As prompting becomes more widely adopted, the complexity and impact of certain use cases will increase. Dedicated prompt engineers will focus on high-stakes, enterprise-scale implementations where consistency, reliability, and accountability are required.

For example, prompts embedded within operational workflows used by large groups and relied upon for mission-critical decisions must perform predictably at scale. These are not one-off interactions. They are systems.

In these environments, prompt engineers play a central role in:

• Designing prompts that scale across users and use cases
• Ensuring outputs remain consistent under changing conditions
• Validating prompt performance before and after deployment
• Identifying and addressing edge cases or failure modes

In regulated industries such as insurance, this responsibility extends further. Enterprise-grade prompts, as well as prompts generated by LLMs, will increasingly require governance. Prompt engineers will be responsible for ensuring that prompts remain valid over time, reflect current business and regulatory conditions, and do not produce unintended or non-compliant outputs. This includes ongoing review, validation, and, when necessary, remediation. Thus, rather than disappear, the role evolves from prompt creation to prompt oversight and control.

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Summary

Effective prompting is a vital component of implementing LLM solutions in the insurance industry. To keep up with the current state of AI technology, insurers should look to develop their prompt engineering capabilities as a core organizational skill.

While knowledge workers across all domains will need to learn prompting skills to effectively use LLM-based tools (general prompting), prompt engineers will not disappear. Their responsibilities will shift toward ensuring that prompting systems operate reliably, consistently, and at scale.

As AI adoption continues to accelerate, the quality of interaction—not just the capabilities of the model—will determine outcomes.

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About Lazarus AI

Lazarus AI develops enterprise-grade AI systems for the insurance industry, public sector, and beyond. Our Applied Intelligence Engine (AIE) enables organizations to eliminate their processing bottlenecks and provides rapid time to value, allowing our customers to compete more effectively with reduced cost, lower risk, and greater speed.

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