Training course · Prompt Engineering · Finance & Treasury

Prompt Engineering for Finance 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 Finance Teams” is designed for roles such as CFOs and heads of finance, Treasury and cash managers and Financial analysts and FP&A teams and shows step by step how to use Prompt Engineering productively, safely and transparently in Finance & Treasury – 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.

Finance and treasury teams handle large volumes of data, tight closing deadlines and strict demands for traceability. Generative AI is reshaping this work: AI assistants summarise bank statements, loan agreements and covenant clauses, draft commentary on cash flow variances and support scenario analysis on interest and exchange rates. AI agents and automation take over recurring steps such as matching payment advices, preparing the liquidity forecast or assembling management reports. What matters is that finance professionals review the output, connect data sources properly and define clear approval workflows. That frees finance teams to focus on analysis, steering and advising management instead of copying figures between spreadsheets, the ERP system and banking portals.

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?

  • CFOs and heads of finance
  • Treasury and cash managers
  • Financial analysts and FP&A teams
  • Corporate finance and M&A specialists
  • Payments and banking operations staff

Typical challenges we address

  • Consolidating the liquidity forecast from many sources such as ERP, bank accounts and business units
  • Explaining and commenting on budget-to-actual variances quickly
  • Reviewing loan agreements, covenants and bank correspondence under time pressure
  • Producing reports for executives, the supervisory board and lenders at short notice

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 assistants effectively for liquidity planning, variance analysis and financing documents.
  • Automate recurring treasury tasks with clear review and approval steps.
  • Critically assess AI output and keep financial data confidential and audit-proof.

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: Finance & Treasury
  • Cash flow commentary
  • Financing agreement analysis
  • Scenarios and sensitivities
  • Management and board reporting
  • Automated remittance matching
  • Policy and process documentation
Module 7 Data protection, quality & transfer to daily work
  • Financial and banking data is confidential and may constitute inside information under MAR; only use approved AI environments with a data processing agreement and GDPR-compliant data handling. Payment approvals must remain under the four-eyes principle and must never be triggered autonomously by AI agents.
  • 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

Cash flow commentary

An AI assistant analyses variances between forecast and actual cash positions and drafts commentary on the main drivers, such as customer receipts, supplier payments or tax dates, which the team then reviews and refines.

Financing agreement analysis

Loan agreements, promissory notes or lease contracts are summarised: maturities, interest mechanics, covenants, termination rights and reporting obligations are extracted into a structured overview for treasury and controlling.

Scenarios and sensitivities

AI helps draft scenario narratives and spreadsheet formulas for interest rate, currency and commodity sensitivities, for example showing how rising reference rates would affect financing costs and covenant ratios.

Management and board reporting

From KPIs, tables and bullet points, an AI assistant produces concise executive summaries, slide drafts and explanatory notes for management, the board or lenders, tailored to audience and level of detail.

Automated remittance matching

An automation workflow reads remittance advices from emails and PDFs, matches them to open items and flags discrepancies for manual review before the data is posted to the ERP or cash management system.

Policy and process documentation

Treasury policies, payment approval processes and control descriptions are structured and harmonised with AI support from notes, existing documents and interviews, making them clear and audit-ready.

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 hands-on experience in financial planning, treasury or reporting and are confident using spreadsheets.

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

Financial and banking data is confidential and may constitute inside information under MAR; only use approved AI environments with a data processing agreement and GDPR-compliant data handling. Payment approvals must remain under the four-eyes principle and must never be triggered autonomously by AI agents.

Suggested dates

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

  • Mon, 23 November 2026 Online Request
  • Fri, 11 December 2026 Bremen Request
  • Thu, 7 January 2027 Augsburg Request
  • Fri, 15 January 2027 Online Request
  • Tue, 19 January 2027 Salzburg Request
  • Mon, 25 January 2027 Online 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.

Can an AI assistant approve payments on its own?

No. AI can prepare payment proposals, match remittances and flag anomalies, but payment approval stays within your authorisation and four-eyes framework. In the course we show how to design automations so that people remain at the critical control points and every action is logged and traceable for auditors.

How should we handle confidential financial data in AI tools?

Start with an approved enterprise environment that has a contractual data processing agreement and does not use your inputs for model training. Add rules for pseudonymisation, such as masking account numbers or counterparties. We discuss practical guidelines that you can agree directly with IT, data protection and compliance.

How much does “Prompt Engineering for Finance 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.

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