Training course · Prompt Engineering · Accounting

Prompt Engineering for Accounting Teams

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 Accounting Teams” is a hands-on course for roles such as General ledger and accounts payable accountants, Accounts receivable specialists and Financial accountants preparing statements. Rather than learning Prompt Engineering in the abstract, you work on tasks from your daily routine – with examples, exercises and templates from Accounting.

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.

Accounting is shaped by recurring processes, high document volumes and strict requirements for proper bookkeeping. Generative AI and automation provide real relief: AI assistants extract data from supplier invoices, suggest account assignments, explain transactions under local GAAP or IFRS and draft queries to suppliers or internal departments. AI agents support account reconciliations, the month-end close and the preparation of documents for external auditors and tax advisers. Responsibility for correct postings stays with people, but routine work shrinks, leaving time for exceptions, process improvement and analysis. The prerequisites are clear review steps, documented procedures and tools that respect requirements for traceability and immutability, such as the German GoBD principles.

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?

  • General ledger and accounts payable accountants
  • Accounts receivable specialists
  • Financial accountants preparing statements
  • Heads of accounting
  • Shared service centre staff

Typical challenges we address

  • Processing high volumes of documents in many formats on time
  • Resolving reconciliations and open items during the month-end close
  • Accounting correctly for complex transactions under GAAP, IFRS and VAT rules
  • Providing complete, traceable records for audits and tax inspections

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 for document processing, account assignment suggestions and reconciliations with clear review steps.
  • Use AI assistants to clarify GAAP, IFRS and VAT questions and verify the answers professionally.
  • Design AI-supported processes that meet bookkeeping compliance requirements and are fully documented.

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: Accounting
  • Invoice data extraction
  • Account assignment and guidance
  • Reconciliation support
  • Dunning and supplier communication
  • Close checklists and procedures
  • Audit request preparation
Module 7 Data protection, quality & transfer to daily work
  • AI-supported accounting processes must meet bookkeeping requirements such as the German GoBD, in particular traceability, immutability and up-to-date procedural documentation. Invoices and bank data contain personal data, so processing them in AI tools requires a GDPR-compliant data processing agreement.
  • 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

Invoice data extraction

AI reads incoming invoices in PDF or e-invoicing formats, checks mandatory VAT details, suggests supplier, GL account and cost centre and flags incomplete or unusual documents for review.

Account assignment and guidance

An AI assistant explains transactions such as prepayments, provisions or capitalisation of assets, proposes possible journal entries and points to the relevant rules, which you then verify professionally.

Reconciliation support

Automations compare bank transactions with receivables and payables, match payments to open items and produce a list of unexplained differences with suggested explanations for the month-end close.

Dunning and supplier communication

AI drafts polite, factual payment reminders, queries about missing purchase order numbers or credit note requests and adapts tone and language for domestic and international customers and suppliers.

Close checklists and procedures

From existing notes, AI creates structured closing checklists, work instructions and sections of the procedural documentation required for compliant bookkeeping, which the team reviews and maintains.

Audit request preparation

When auditors or tax inspectors send requests, AI helps structure the questions, map the evidence required and draft clear explanations of postings, estimates and judgements.

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 have a basic knowledge of financial accounting and work regularly with an ERP or accounting system.

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

AI-supported accounting processes must meet bookkeeping requirements such as the German GoBD, in particular traceability, immutability and up-to-date procedural documentation. Invoices and bank data contain personal data, so processing them in AI tools requires a GDPR-compliant data processing agreement.

Suggested dates

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

  • Thu, 26 November 2026 Online Request
  • Wed, 2 December 2026 Hanover Request
  • Fri, 18 December 2026 Online Request
  • Fri, 22 January 2027 Munich Request
  • Tue, 2 February 2027 Online Request
  • Fri, 19 February 2027 Stuttgart 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.

Are AI-supported postings compliant?

AI provides suggestions, but postings are made in your compliant accounting system after review by staff. What matters are documented review steps, logging and updated procedural documentation. In the course we work out how to embed AI steps cleanly in your process and make them transparent to auditors.

How well does AI handle invoices with different layouts?

Modern models usually process varied layouts and structured e-invoices reliably, but they make mistakes with blurred scans or unusual formats. Plausibility checks such as total reconciliation, VAT rate validation and supplier master data matching are therefore essential. We show how to build these controls into your workflow.

How much does “Prompt Engineering for Accounting Teams” 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.

More courses for Accounting Teams

More Prompt Engineering courses

All 45 Prompt Engineering courses

Prompt Engineering · MunichPrompt Engineering · SalzburgPrompt Engineering · BerlinPrompt Engineering · AugsburgPrompt Engineering · HanoverPrompt Engineering · BremenPrompt Engineering · StuttgartPrompt Engineering · KaiserslauternPrompt Engineering · Online