Training course · Prompt Engineering · Insurance

Prompt Engineering for Insurance Professionals

Vendor-neutral prompt engineering: roles, context, few-shot examples, prompt chains, prompt libraries and systematic quality checks.

Duration
1 day (7 hrs)
Format
Online or on-site
Language
German or English
Participants
open dates or exclusive for your team
Certificate
Certificate of attendance

Overview

“Prompt Engineering for Insurance Professionals” is designed for roles such as Claims handlers and benefits specialists, Underwriters and risk surveyors and Broker and agency service staff and shows step by step how to use Prompt Engineering productively, safely and transparently in Insurance – from the fundamentals to advanced workflows.

Whether you use ChatGPT, Claude, Copilot, Gemini or Le Chat, the quality of the output depends largely on how the task is framed. Prompt engineering is the skill of conveying goal, context, constraints and desired format to a language model so that it reliably produces usable results. In this vendor-neutral practical day you learn proven techniques that work across all mainstream AI assistants: defining roles and audiences, selecting relevant context, working with examples and breaking complex tasks down into traceable steps. You try out every technique directly on tasks from your own working day.

The second part focuses on repeatability and quality across the team. You develop prompt templates with placeholders, build multi-step prompt chains for analysis, writing and review processes, and set up a shared prompt library. You learn to evaluate output systematically – with review criteria, test cases and targeted self-checks by the model – and to avoid typical pitfalls such as hallucinations, ambiguity and missing context. By the end you have a structured approach that works regardless of the tool you use and continues to pay off as new models arrive.

Insurers process large volumes of applications, claims notifications, expert reports and correspondence every day. Generative AI targets exactly that workload: AI assistants summarise claim files, extract data from loss adjuster reports and invoices, pre-check coverage questions against policy wordings and draft clear customer letters. AI agents and automation speed up intake, inbox classification and broker service. In underwriting, AI helps evaluate risk information and prepare quotes. At the same time, supervisors and customers expect transparency and fairness, especially in claims and pricing decisions. Success comes from using AI to assist claims handlers and underwriters, documenting decisions transparently and giving health and personal data the extra protection it requires.

The course is delivered live – either online in a virtual classroom or on-site in our training rooms in Munich, Salzburg, Berlin, Augsburg, Hanover, Bremen, Stuttgart and Kaiserslautern. We can also run it exclusively for your team.

Who is this course for?

  • Claims handlers and benefits specialists
  • Underwriters and risk surveyors
  • Broker and agency service staff
  • Product managers and actuarial teams
  • Process and digital transformation leads

Typical challenges we address

  • Getting to grips quickly with large claim files containing reports, photos and correspondence
  • Checking coverage against complex policy wordings
  • Handling surges in incoming mail, for example after severe weather events
  • Writing customer communication that is clear yet legally sound

What you will be able to do

  • You understand how language models process prompts and derive effective wording from this.
  • You structure prompts with role, context, task, constraints and output format.
  • You use few-shot examples and prompt chains for complex, multi-step tasks.
  • You create reusable templates and build a shared prompt library.
  • Use AI as an assistant in claims, benefits and new business processing with clear decision boundaries.
  • Automate inbox handling and standard correspondence in a traceable way.
  • Take insurance supervision requirements and the protection of health data into account when using AI.

Course outline

Module 1 How language models process prompts
  • Core principles of language models: probabilities, context window, limitations
  • Why wording, order and examples change the result
  • What causes hallucinations and how prompts reduce them
  • Differences and common ground between mainstream AI assistants
Module 2 Building blocks of an effective prompt
  • Defining role, audience, task, context and output format
  • Stating constraints, exclusions and quality criteria
  • Structured input with sections, delimiters and tags
  • Asking the model to raise questions instead of making assumptions
Module 3 Advanced techniques
  • Zero-shot, one-shot and few-shot: setting style and structure with examples
  • Requesting step-by-step reasoning for complex tasks
  • Prompt chains: separating research, drafting, review and revision
  • Using change of perspective, counter-arguments and model self-critique
Module 4 Templates and prompt libraries
  • Creating reusable templates with placeholders and variables
  • Storing persistent instructions in projects, custom assistants and agents
  • Building, naming, maintaining and sharing a team prompt library
  • Documenting templates: purpose, inputs, expected output, owner
Module 5 Quality assurance and responsible use
  • Defining review criteria and scoring rubrics for AI output
  • Setting up test cases and systematically comparing and improving prompts
  • Verifying facts, figures and sources; establishing the four-eyes principle
  • Data protection, confidential content and transparency under GDPR and the EU AI Act
Module 6 Practice lab: Insurance
  • Claim file summaries
  • Coverage pre-check
  • Inbox classification
  • Customer letters
  • Underwriting support
  • Broker support
Module 7 Data protection, quality & transfer to daily work
  • Insurers are subject to supervisory rules such as the German VAG, DORA and the GDPR; health data is a special category under Article 9 GDPR and further protected by professional secrecy rules. AI used for risk assessment and pricing in life and health insurance is high-risk under the EU AI Act.
  • Ground rules for safe AI use in your team
  • Checking results: facts, sources, tests and the four-eyes principle
  • Personal action plan for the next 30 days
  • Open questions, outlook and resources for further learning

Practical scenarios from your field

Claim file summaries

An AI assistant turns the first notification of loss, expert reports, invoices and correspondence into a structured summary with a timeline, open points and potential inconsistencies for the claims handler.

Coverage pre-check

AI compares the described incident with the relevant policy clauses, quotes the passages and prepares an assessment that the claims handler evaluates and decides on.

Inbox classification

Incoming emails, letters and forms are sorted by request type, line of business and urgency, policy numbers are recognised and items routed to the right team with a suggested next action.

Customer letters

AI drafts clear letters on claims decisions, requests for documents or policy changes, while keeping mandatory wording and the company's tone of voice.

Underwriting support

For commercial risks, AI summarises risk questionnaires, survey reports and loss histories and compiles follow-up questions and relevant risk features for the underwriting decision.

Broker support

A knowledge assistant answers broker and agent questions about products, underwriting guidelines and processes based on approved documents, always citing the source.

Prerequisites

Some initial experience with at least one AI assistant such as ChatGPT, Claude, Copilot or Gemini is helpful; no technical background is required. You work for an insurer, broker or agency and know the processes in your line of business.

Technical requirements

A laptop with an up-to-date browser and access to at least one AI assistant, ideally the tool approved within your organisation.

Data protection & compliance

Insurers are subject to supervisory rules such as the German VAG, DORA and the GDPR; health data is a special category under Article 9 GDPR and further protected by professional secrecy rules. AI used for risk assessment and pricing in life and health insurance is high-risk under the EU AI Act.

Suggested dates

All dates are suggestions – your preferred date is always possible.

  • Thu, 26 November 2026 Online Request
  • Thu, 10 December 2026 Salzburg Request
  • Thu, 17 December 2026 Online Request
  • Tue, 12 January 2027 Bremen Request
  • Fri, 22 January 2027 Online Request
  • Thu, 4 February 2027 Augsburg Request

Frequently asked questions

Do these techniques work with every AI assistant?

Yes, the course is deliberately vendor-neutral. The principles – a clear task, relevant context, examples, structured output and review steps – work equally well in ChatGPT, Claude, Copilot, Gemini, Le Chat and other tools. Where assistants differ in detail, for example with projects or custom assistants, we show the corresponding features in each.

Won't better models soon make prompt engineering obsolete?

Models are getting better at interpreting vague requests, but they still cannot know what you do not tell them: audience, quality standards, internal requirements or relevant background. Good prompts are therefore mainly good task descriptions. Templates, prompt chains and quality checks also keep their value for teams as new models arrive.

What is a prompt library and how do we maintain it as a team?

A prompt library is an organised collection of proven templates for recurring tasks, each with its purpose, required inputs, a sample result and an owner. It can live in a shared document, a wiki or directly in the AI tool's projects. In the course you set up a first library and agree simple rules for maintenance and approval.

May AI decide on claims?

In practice, AI should prepare decisions, not make them. Automated individual decisions with legal effect are subject to specific requirements under Article 22 GDPR. We show how to design processes so that claims handlers decide on a sound basis and the reasoning is documented transparently.

How should we handle health data?

Health data from health, life or disability insurance needs special protection. Use only approved, contractually secured AI environments, minimise the data you share and pseudonymise consistently. In the course we discuss concrete rules for your processes. Data processing agreements and deletion concepts must also be considered.

How much does “Prompt Engineering for Insurance Professionals” cost?

The course costs a flat €949 plus VAT per participant – whether online or on-site. This includes 1 training day(s) with a live trainer, course materials, a lab environment and a certificate of attendance.

Can I suggest my own date?

Yes. Our suggested dates are just a guide. In the request form you can always describe your preferred date or period in free text – we check availability and confirm within one working day.

Is the course online or on-site?

Both are possible: live online in a virtual classroom or on-site in Munich, Salzburg, Berlin, Augsburg, Hanover, Bremen, Stuttgart or Kaiserslautern. We can also run the course exclusively for your team.

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