Training course · Prompt Engineering · Consulting

Prompt Engineering for Consultants

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 Consultants” is a hands-on course for roles such as Management and strategy consultants, IT and process consultants and In-house consulting teams. Rather than learning Prompt Engineering in the abstract, you work on tasks from your daily routine – with examples, exercises and templates from Consulting.

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.

Consulting thrives on rapid onboarding, structured analysis and persuasive communication – exactly where generative AI makes a difference. Consultants use AI assistants to get up to speed on new industries, synthesise interview notes, build hypothesis trees, analyse client data and develop storylines for steering committees. AI agents support desk research, document comparison and first slide drafts. At the same time, AI is reshaping the business model: clients expect faster results and ask what value consultants add beyond what they could generate with AI themselves. Consultants who use AI confidently shift their time from grunt work towards judgement, implementation support and relationship building.

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?

  • Management and strategy consultants
  • IT and process consultants
  • In-house consulting teams
  • Independent consultants
  • Analysts and junior consultants

Typical challenges we address

  • Short ramp-up time for new clients, industries and topics
  • Large volumes of interviews, documents and data under time pressure
  • Heavy effort for slides, reports and proposals
  • Confidentiality of client information when using AI

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.
  • Accelerate onboarding, research and synthesis in consulting projects with AI.
  • Develop structured storylines, slides and proposals with AI support.
  • Use AI in client work in line with confidentiality obligations and communicate its use transparently.

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: Consulting
  • Rapid onboarding
  • Synthesising interviews
  • Hypotheses and issue trees
  • Storylines and slides
  • Proposals and bids
  • Faster data analysis
Module 7 Data protection, quality & transfer to daily work
  • Client information is usually covered by confidentiality agreements and may only be processed in AI environments cleared contractually and under data protection law. AI use should be agreed with the client, and personal interview data must be handled in line with the GDPR.
  • 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

Rapid onboarding

AI produces industry overviews, explains terminology, value chains and typical KPIs and drafts key questions for the kick-off – as a starting point to be verified with reliable sources.

Synthesising interviews

Notes from stakeholder interviews are organised by theme, pain point and quote, patterns and contradictions are drawn out and the results prepared as a basis for hypotheses.

Hypotheses and issue trees

Problems are broken down with AI into mutually exclusive, collectively exhaustive sub-questions, hypotheses are formulated and the required analyses and data sources derived. This quickly yields a robust workplan for the team.

Storylines and slides

Analysis results become pyramid-structured storylines, action titles and slide drafts, which the team then sharpens and builds out in the house style. The core message stays at the centre of every slide.

Proposals and bids

Tender documents are analysed, requirements turned into checklists and proposal sections on approach, work packages and team profile written with a clear focus. The client's evaluation criteria are addressed directly.

Faster data analysis

Client data is cleaned, analysed and visualised with AI support, for example for benchmarks, cost analyses or process KPIs, with documented calculation steps. Sanity checks safeguard quality before client presentations.

Prerequisites

Some initial experience with at least one AI assistant such as ChatGPT, Claude, Copilot or Gemini is helpful; no technical background is required. Experience in consulting projects or analytical project roles; no programming skills required.

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

Client information is usually covered by confidentiality agreements and may only be processed in AI environments cleared contractually and under data protection law. AI use should be agreed with the client, and personal interview data must be handled in line with the GDPR.

Suggested dates

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

  • Mon, 23 November 2026 Online Request
  • Fri, 4 December 2026 Munich Request
  • Fri, 11 December 2026 Online Request
  • Wed, 20 January 2027 Hanover Request
  • Tue, 2 February 2027 Online Request
  • Wed, 10 February 2027 Berlin 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 I use client data in AI tools?

Only if the contract, NDA and your firm's policies permit it and the tool is covered contractually. It is often sensible to agree AI use with the client. The course shows how to work with anonymised data and set clear rules for project teams.

Will AI make consultants redundant?

Grunt work such as desk research or first slide drafts becomes cheaper. Judgement, industry experience, facilitation and implementation support remain in demand. Firms that use AI well deliver faster and can invest more time in real impact for clients. Value shifts towards advice quality and outcomes.

How much does “Prompt Engineering for Consultants” 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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