Training course · RAG & LLM Applications · Citizen Developers & Low-Code

RAG & LLM Applications for Citizen Developers

Develop RAG and LLM applications: embeddings, vector databases, chunking, retrieval, evaluation, cost control and data protection.

Duration
2 days (14 hrs)
Format
Online or on-site
Language
German or English
Participants
open dates or exclusive for your team
Certificate
Certificate of attendance

Overview

“RAG & LLM Applications for Citizen Developers” is designed for roles such as Business users with automation ideas in administration, sales, procurement or HR, Power users of Excel, Power Platform or similar low-code tools and Process owners and business analysts and shows step by step how to use RAG & LLM Applications productively, safely and transparently in Citizen Developers & Low-Code – from the fundamentals to advanced workflows.

Retrieval-augmented generation connects language models to your own knowledge: manuals, contracts, tickets, wikis or product data are made searchable and handed to the model as context at runtime. The result is assistants that give grounded answers with citations instead of hallucinating. In this two-day course you build a complete RAG pipeline, from document preparation through embeddings and a vector database to answer generation, and you learn which levers actually determine quality.

Day two takes you from prototype to robust application. You compare chunking strategies, combine semantic and lexical search, apply reranking, metadata filters and query rewriting, and measure the effect with systematic evaluations. You also cover prompt caching, cost control, latency optimisation, document-level access control and privacy-compliant operation with an eye on GDPR and the EU AI Act. By the end you know when classic RAG is enough, when a large context window or agentic retrieval is the better option, and how to justify your architecture choices.

Citizen developers are business users who build their own applications and automations without being professional software engineers. Until recently, low-code platforms quickly hit their limits whenever custom logic, integrations or data transformations were needed. AI coding tools push that boundary much further: a plain-language description becomes forms, workflows, scripts and small web apps, and AI explains error messages, formulas and expressions in understandable terms. Responsibility grows at the same time, because home-built solutions also process company data and need maintenance. In this course you learn to use vibe coding and AI agents to build useful solutions for your department, combine them sensibly with low-code platforms and stay within your IT governance rules.

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?

  • Business users with automation ideas in administration, sales, procurement or HR
  • Power users of Excel, Power Platform or similar low-code tools
  • Process owners and business analysts
  • Members of digitalisation and innovation teams
  • IT leads setting up a citizen development programme

Typical challenges we address

  • Recurring manual tasks that IT has no capacity to automate
  • Low-code platforms hit limits with custom logic, integrations or data preparation
  • Little know-how on testing, documenting and running solutions safely
  • Risk of shadow IT without alignment with data protection and IT security

What you will be able to do

  • You build a complete RAG pipeline from document preparation to answers with citations.
  • You choose embedding model, chunking strategy and vector database on solid grounds.
  • You improve retrieval quality with hybrid search, reranking and metadata filters.
  • You measure application quality with systematic evaluations and detect regressions.
  • Build your own automations, forms and small applications for your department with AI support.
  • Combine low-code platforms with generated code for logic, data preparation and integrations.
  • Document, test and run your solutions in line with your IT governance rules.

Course outline

Day 1

Module 1 Foundations of RAG and LLM applications
  • Anatomy of a RAG pipeline: ingestion, indexing, retrieval, generation
  • Understanding embeddings: semantic similarity, dimensions, model selection
  • Weighing RAG against fine-tuning and long context windows
  • Structured outputs, citations and dealing with hallucinations
Module 2 Document preparation and chunking
  • Reliably extracting PDFs, Office documents, HTML and tables
  • Chunking strategies: fixed size, structure-aware, semantic, with overlap
  • Enriching metadata and generating contextual summaries per chunk
  • Incremental updates and deletion of documents in the index
Module 3 Vector databases and retrieval strategies
  • Comparing vector databases and extensions such as pgvector, Qdrant, Weaviate or OpenSearch
  • Hybrid search combining vector and full-text search with BM25
  • Reranking, metadata filtering and query rewriting
  • Agentic retrieval: letting the model decide on search steps and tools
Module 4 Evaluation and quality assurance
  • Building and maintaining test sets from real user questions
  • Retrieval metrics such as recall and precision, and answer metrics such as faithfulness
  • Using and calibrating LLM-as-a-judge sensibly
  • Regression testing when chunking, model or prompt changes

Day 2

Module 5 Operations, cost and data protection
  • Prompt caching, batch processing and model choice to cut costs
  • Optimising latency and streaming responses
  • Document-level permissions, tenant isolation and protection against prompt injection
  • Data protection: data residency, processing agreements, deletion policies, GDPR and the EU AI Act
Module 6 Practice lab: Citizen Developers & Low-Code
  • Automating workflows
  • Building forms and small apps
  • Explaining formulas and expressions
  • Cleaning and transforming data
  • Connecting to interfaces
  • Documenting and testing solutions
Module 7 Data protection, quality & transfer to daily work
  • Home-built solutions also process company and employee data: you learn to respect GDPR requirements, IT approval processes, secure handling of credentials and the licence terms of platforms and generated code, so that no shadow IT emerges.
  • 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

Automating workflows

Describe approval processes, notifications and data transfers between email, spreadsheets, forms and business applications on low-code platforms, and have AI build the matching flows, expressions and conditions.

Building forms and small apps

Generate data entry forms, checklists or booking apps from a description, complete with validation, mandatory fields and simple data storage, and refine them step by step based on colleagues’ feedback.

Explaining formulas and expressions

Have complex Excel formulas, Power Fx or flow expressions explained in plain terms, track down errors and reliably write new calculations from a business description.

Cleaning and transforming data

Merge exports from different systems, remove duplicates, standardise formats and generate Python scripts or Power Query steps that make the preparation repeatable. Monthly preparation then takes minutes, not hours.

Connecting to interfaces

Use AI to understand how a REST interface works, configure connectors and set up simple API calls that bring data from business systems into your own solutions.

Documenting and testing solutions

Produce user guides, short technical documentation and test cases for home-built applications so colleagues and IT can understand, take over and keep running the solution.

Possible hands-on projects

Leave and cover planner

A small web app where team members enter absences, assign cover and give the team lead a monthly overview. Overlaps are flagged automatically.

Incoming invoice checker

A script that reads incoming PDF invoices, checks mandatory details and transfers the data into a structured spreadsheet for approval.

Request tracker

A simple ticketing tool for internal requests with a form, status history, assignments and automatic email notifications. Everyone keeps track of open and completed requests.

Prerequisites

Solid programming skills in Python (or TypeScript as an alternative), basic knowledge of databases and REST APIs, and some experience with LLM APIs. You are confident with office applications and spreadsheets; no programming skills are required, though experience with a low-code platform helps.

Technical requirements

A laptop with Python, Git, Docker and a code editor, plus API access to at least one LLM and embedding provider. Credentials for the exercises can be provided if needed.

Data protection & compliance

Home-built solutions also process company and employee data: you learn to respect GDPR requirements, IT approval processes, secure handling of credentials and the licence terms of platforms and generated code, so that no shadow IT emerges.

Suggested dates

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

  • Wed, 25 November 2026 – Thu, 26 November 2026 Online Request
  • Wed, 9 December 2026 – Thu, 10 December 2026 Salzburg Request
  • Thu, 17 December 2026 – Fri, 18 December 2026 Online Request
  • Mon, 18 January 2027 – Tue, 19 January 2027 Bremen Request
  • Tue, 2 February 2027 – Wed, 3 February 2027 Online Request
  • Tue, 9 February 2027 – Wed, 10 February 2027 Augsburg Request

Frequently asked questions

When is RAG better than fine-tuning?

RAG is the right choice when a model needs access to current, extensive or frequently changing knowledge and should cite its sources. Fine-tuning mainly changes style, format or specialised behaviour. In many projects RAG is the faster, cheaper and more controllable route, sometimes complemented by fine-tuning for niche tasks.

Which vector database should we use?

That depends on data volume, existing infrastructure, filtering needs and operating model. If you already run PostgreSQL, pgvector often takes you a long way, while specialised systems shine with large datasets and hybrid search. In the course you compare several options hands-on and derive criteria for your own decision.

How can RAG be run in a GDPR-compliant way?

Key factors are choosing providers with suitable data residency and a data processing agreement, enforcing document-level permissions, having a deletion policy for the index and logs, and minimising personal data. For highly sensitive data, self-hosted models are also an option. We work through these choices using realistic architectures.

Do I need programming skills to work with AI as a citizen developer?

No. You describe what your solution should do and the AI generates forms, flows, formulas or scripts. What matters is stating your requirements clearly and checking results systematically. In the course you learn the essential concepts such as data, logic, interfaces and testing so you can steer precisely and narrow down errors yourself.

How do we avoid shadow IT?

With clear rules rather than bans: approved platforms and tools, defined data classes, a simple register of home-built solutions and a route for handing over to IT once an application becomes business-critical. In the course you learn to apply such rules, and IT leads gain ideas for setting up a citizen development programme.

How much does “RAG & LLM Applications for Citizen Developers” cost?

The course costs a flat €949 plus VAT per participant – whether online or on-site. This includes 2 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.

More courses for Citizen Developers

More RAG & LLM Applications courses

All 29 RAG & LLM Applications courses

RAG & LLM Applications · MunichRAG & LLM Applications · SalzburgRAG & LLM Applications · BerlinRAG & LLM Applications · AugsburgRAG & LLM Applications · HanoverRAG & LLM Applications · BremenRAG & LLM Applications · StuttgartRAG & LLM Applications · KaiserslauternRAG & LLM Applications · Online