Training course · Prompt Engineering · E-Commerce

Prompt Engineering for E-Commerce 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 E-Commerce Teams” is a hands-on course for roles such as E-commerce managers and shop owners, Marketplace and category managers and Product data and PIM managers. Rather than learning Prompt Engineering in the abstract, you work on tasks from your daily routine – with examples, exercises and templates from E-Commerce.

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.

In e-commerce, every product page, search query and customer enquiry counts. Generative AI opens up new possibilities for online retailers and brands: AI assistants create and optimise product descriptions, translate catalogues, structure product attributes, analyse customer reviews and support marketplace listings. Automation and AI agents maintain product data, monitor prices and stock, answer routine questions and prepare campaigns for sales events. Shop analytics also become more accessible, because questions about conversion, baskets and returns can be asked in natural language. Success requires clean product data, clear quality rules for generated copy and compliance with the many information obligations in online retail. Then AI becomes a lever for visibility, conversion and efficient operations.

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?

  • E-commerce managers and shop owners
  • Marketplace and category managers
  • Product data and PIM managers
  • Digital marketing and SEO specialists
  • Founders and owners of online shops

Typical challenges we address

  • Keeping thousands of product records complete, consistent and multilingual
  • Increasing visibility in search engines and on marketplaces
  • Analysing and handling returns, reviews and customer enquiries efficiently
  • Delivering sales events and range changes with limited resources

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 and maintain product content efficiently, in several languages and at consistent quality with AI.
  • Analyse shop, review and returns data with AI and derive concrete actions.
  • Design AI-supported shop processes in compliance with consumer protection and data protection law.

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: E-Commerce
  • Product description generation
  • Product data enrichment
  • Catalogue translation
  • Reviews and return reasons
  • Marketplace listing optimisation
  • Natural language shop analytics
Module 7 Data protection, quality & transfer to daily work
  • Online retail is subject to extensive consumer information duties, price indication rules, unfair competition law for product claims and the Digital Services Act for platforms; AI-generated copy must not contain misleading statements. Customer data from orders and reviews must be processed 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

Product description generation

Using manufacturer data, attributes and brand guidelines, AI creates search-friendly product copy, bullet points and meta descriptions, which the team spot-checks and approves.

Product data enrichment

AI extracts attributes such as material, dimensions or compatibility from data sheets, normalises spelling and assigns products to the right category to improve filters and on-site search.

Catalogue translation

Product copy is translated and localised for new markets using a glossary for brand terms and units, with native-speaker review for key pages.

Reviews and return reasons

Customer reviews and return reasons are clustered by topics such as fit, quality or delivery, so buying, product development and content teams can make targeted improvements.

Marketplace listing optimisation

AI adapts titles, bullet points and keywords to the requirements of individual marketplaces and checks listings for missing mandatory information or policy violations.

Natural language shop analytics

Exports from the shop system and web analytics are analysed with AI, for example conversion rate by category, basket abandonment or best-sellers, including hypotheses for optimisation tests.

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 an online shop, on marketplaces or in digital retail and have a basic understanding of your shop 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

Online retail is subject to extensive consumer information duties, price indication rules, unfair competition law for product claims and the Digital Services Act for platforms; AI-generated copy must not contain misleading statements. Customer data from orders and reviews must be processed in line with the GDPR.

Suggested dates

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

  • Thu, 26 November 2026 Online Request
  • Thu, 10 December 2026 Salzburg Request
  • Mon, 14 December 2026 Online Request
  • Mon, 11 January 2027 Online Request
  • Wed, 20 January 2027 Bremen Request
  • Thu, 4 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.

Does AI-generated product copy hurt SEO?

Not if it is helpful and unique. The problem is interchangeable copy that merely rephrases manufacturer information. Precise attributes, usage examples, answers to frequent customer questions and a quality check produce copy that convinces search engines and shoppers alike. We practise exactly these prompting and review routines.

Who is liable for errors in AI-generated copy?

The retailer is liable for all product information, regardless of how it was created. Incorrect dimensions, materials or efficacy claims can lead to legal warnings. AI copy should therefore be based only on verified product data, and sensitive claims such as health or environmental statements should be approved separately.

How much does “Prompt Engineering for E-Commerce 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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