Training course · Prompt Engineering · Research & Science

Prompt Engineering for Researchers

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 Researchers” is designed for roles such as Doctoral candidates, Research associates and postdocs and Professors and research group leaders and shows step by step how to use Prompt Engineering productively, safely and transparently in Research & Science – 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.

Academic work is shaped by literature reviews, data analysis, grant writing and publication pressure. Generative AI can help at every stage: navigating and summarising the literature, sharpening research questions, writing analysis code, polishing manuscripts and preparing funding applications. AI agents can automate recurring steps such as data cleaning or cross-checking references. At the same time, AI touches core questions of research integrity: who is an author, how is AI use disclosed, how do you prevent fabricated references? Researchers therefore need more than prompting tricks – they need a methodologically sound, transparent and reproducible way of working with AI tools.

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?

  • Doctoral candidates
  • Research associates and postdocs
  • Professors and research group leaders
  • Research offices and grant managers
  • Corporate R&D teams

Typical challenges we address

  • An ever-growing body of literature that is hard to keep up with
  • Heavy writing load for proposals, reports and papers
  • Data analysis that requires programming skills
  • Uncertainty about AI rules set by journals, funders and universities

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 literature work and writing without breaching research integrity standards.
  • Produce traceable analysis code with AI support and document it reproducibly.
  • Disclose AI use transparently in line with journal, funder and institutional requirements.

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: Research & Science
  • Navigating the literature
  • Sharpening research design
  • Writing analysis code
  • Revising manuscripts
  • Preparing grant proposals
  • Analysing qualitative data
Module 7 Data protection, quality & transfer to daily work
  • Codes of research integrity apply: AI cannot be an author, its use must be disclosed and all references must be checked against the original. Personal research data, for example from studies involving participants, is subject to the GDPR and ethics approvals; unpublished results and peer reviews must be treated as confidential.
  • 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

Navigating the literature

AI summarises abstracts and full texts, extracts methods, samples and findings into comparison tables and helps formulate search strings for scholarly databases – every source is then verified in the original.

Sharpening research design

Research questions, hypotheses and operationalisations are stress-tested with AI as a critical sparring partner to spot gaps, confounders and alternative explanations early. Control conditions and limits to generalisability are discussed as well.

Writing analysis code

Researchers without deep programming skills use AI to produce Python or R scripts for data cleaning, statistics and visualisation, documenting them clearly to ensure reproducibility.

Revising manuscripts

AI supports language editing, structuring and shortening of academic texts, checks terminology for consistency and helps draft responses to reviewers without taking over authorship. Your argument stays your own.

Preparing grant proposals

Funding guidelines are turned into checklists, work packages and milestones are structured, and sections such as impact plans or lay summaries are drafted in several versions.

Analysing qualitative data

Interview transcripts and open-ended responses are pre-coded and grouped thematically with AI; researchers review, refine and document the coding frame according to established methodology. Inter-coder checks remain essential.

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 of academic work in a discipline; programming skills are helpful but not 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

Codes of research integrity apply: AI cannot be an author, its use must be disclosed and all references must be checked against the original. Personal research data, for example from studies involving participants, is subject to the GDPR and ethics approvals; unpublished results and peer reviews must be treated as confidential.

Suggested dates

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

  • Wed, 25 November 2026 Online Request
  • Mon, 30 November 2026 Hanover Request
  • Mon, 14 December 2026 Online Request
  • Thu, 7 January 2027 Online Request
  • Tue, 19 January 2027 Munich Request
  • Fri, 5 February 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 I use AI to write my PhD thesis?

That depends on your doctoral regulations and institutional policies. AI is often permitted for language editing and brainstorming if its use is disclosed and your own intellectual contribution remains clear. In the course you learn to document use properly and assess common grey areas with confidence.

How do I avoid fabricated references?

Language models can produce plausible-sounding citations that do not exist. Work with tools that query real databases, ask for DOIs and verify every source in the original. The course demonstrates a workflow that combines AI-assisted searching with reliable reference management.

How much does “Prompt Engineering for Researchers” 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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