Training course · Prompt Engineering · Recruiting

Prompt Engineering for Recruiters

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 Recruiters” is a hands-on course for roles such as Recruiters and talent acquisition managers, Sourcers and HR generalists with recruiting responsibility. Rather than learning Prompt Engineering in the abstract, you work on tasks from your daily routine – with examples, exercises and templates from Recruiting.

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.

With talent shortages in many fields, recruiting has become a key business function: candidates must be found, convinced and guided quickly through the process. Generative AI supports recruiters at many points of the candidate journey. AI assistants write job ads that speak to the target group, build search strings for active sourcing, draft personal outreach messages, prepare structured interview guides and summarise interview notes. Automation handles scheduling, acknowledgements and status updates for applicants. At the same time, recruiting is a particularly sensitive area: selection decisions must be non-discriminatory and transparent, and AI systems used to screen candidates face strict requirements. Using AI for communication, structure and efficiency while keeping selection in human hands improves both candidate experience and time to hire.

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?

  • Recruiters and talent acquisition managers
  • Sourcers
  • HR generalists with recruiting responsibility
  • Employer branding leads
  • Recruitment consultants and staffing agencies

Typical challenges we address

  • Finding and approaching suitable candidates in tight labour markets
  • Writing job ads that genuinely reach the target group
  • Running fast application processes with a good candidate experience
  • Making fair, structured and legally sound selection decisions

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.
  • Create targeted job ads, sourcing strategies and outreach messages with AI.
  • Structure selection processes with AI-supported guides and improve the candidate experience.
  • Use AI in recruiting in line with anti-discrimination law, the GDPR and the EU AI Act.

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: Recruiting
  • Job ad optimisation
  • Active sourcing strategies
  • Personal outreach
  • Structured interview guides
  • Candidate communication
  • Recruiting metrics analysis
Module 7 Data protection, quality & transfer to daily work
  • Selection processes must comply with anti-discrimination law such as the German General Equal Treatment Act (AGG); AI must not disadvantage people based on age, gender or origin. Applicant data is protected by the GDPR, solely automated rejections are not permitted under Article 22 GDPR, and AI systems for screening and selecting candidates are 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

Job ad optimisation

An AI assistant turns the role profile and team information into an inclusive job ad tailored to the target group, checks it for gender-neutral language and unnecessary barriers and creates variants for different channels.

Active sourcing strategies

AI builds Boolean search strings, alternative job titles and relevant skills for professional networks and job boards and suggests target companies and communities for direct approach.

Personal outreach

Based on the public profile and the role, AI drafts individual contact messages and follow-ups that make the specific value of the position clear to the person.

Structured interview guides

AI creates competency-based interview questions, rating anchors and case exercises for a role, so that all candidates are assessed against the same criteria.

Candidate communication

Automations send acknowledgements, scheduling proposals and status updates, while AI drafts respectful rejections and offers that recruiters review before sending.

Recruiting metrics analysis

Applicant tracking system exports are analysed for time to hire, channel performance and drop-off rates, bottlenecks identified and improvements proposed.

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 in recruiting or talent acquisition and know your organisation's hiring process.

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

Selection processes must comply with anti-discrimination law such as the German General Equal Treatment Act (AGG); AI must not disadvantage people based on age, gender or origin. Applicant data is protected by the GDPR, solely automated rejections are not permitted under Article 22 GDPR, and AI systems for screening and selecting candidates are high-risk under the EU AI Act.

Suggested dates

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

  • Mon, 23 November 2026 Online Request
  • Wed, 9 December 2026 Stuttgart Request
  • Wed, 13 January 2027 Online Request
  • Wed, 27 January 2027 Berlin Request
  • Mon, 8 February 2027 Online Request
  • Thu, 18 February 2027 Munich 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 pre-screen applications?

Systems that filter or rank applications are classified as high-risk AI under the EU AI Act, with extensive obligations. The GDPR also prohibits solely automated rejections. The course therefore focuses on AI for communication, structure and preparation, while screening and decisions remain with people.

How do we avoid discrimination by AI?

Language models can reproduce biases from their training data, for example in job ad wording. Check outputs specifically for protected characteristics, use structured criteria and never enter sensitive data into AI tools. We practise concrete review questions that you can adopt in your process.

How much does “Prompt Engineering for Recruiters” 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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