Training course · RAG & LLM Applications · Web Development

RAG & LLM Applications for Web 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 Web Developers” is a hands-on course for roles such as Web developers in agencies and in-house teams, Freelancers building websites and web applications and Developers on CMS projects using WordPress, TYPO3 or headless CMS. Rather than learning RAG & LLM Applications in the abstract, you work on tasks from your daily routine – with examples, exercises and templates from Web Development.

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.

Web development today covers far more than HTML, CSS and a bit of JavaScript: responsive layouts, accessibility, SEO, performance, forms, content management systems, APIs, hosting and deployment are all part of the job. In small teams and agencies especially, web developers have to cover all of it. AI coding assistants and agents are a huge lever here: they turn designs into layouts, write semantic HTML and modern CSS with grid and container queries, audit pages for accessibility and Core Web Vitals, build CMS templates and automate deployments. Vibe coding also makes rapid prototypes possible that used to take days. This course teaches you to combine that speed with clean structure, maintainability and quality assurance.

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?

  • Web developers in agencies and in-house teams
  • Freelancers building websites and web applications
  • Developers on CMS projects using WordPress, TYPO3 or headless CMS
  • Career changers with a grounding in HTML, CSS and JavaScript

Typical challenges we address

  • A broad remit from layout and SEO to hosting in small teams
  • Rising expectations around accessibility, performance and privacy
  • Maintaining many client projects built on different technologies and versions
  • Time pressure on relaunches, landing pages and campaign sites

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.
  • Deliver websites and web applications faster and with clean structure using AI support.
  • Use AI deliberately for accessibility, performance and technical SEO.
  • Turn vibe-coded prototypes into maintainable, secure and privacy-compliant projects.

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: Web Development
  • Turning designs into layouts
  • Accessibility audit
  • Performance and Core Web Vitals
  • CMS templates and extensions
  • Technical SEO
  • Forms and integrations
Module 7 Data protection, quality & transfer to daily work
  • Websites are subject to GDPR, ePrivacy rules and, in many cases, accessibility legislation under the European Accessibility Act: generated code must not embed tracking or external resources without consent and must be checked for accessibility. Also watch the licences of fonts, images and code snippets, and keep hosting and CMS credentials out of prompts.
  • 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

Turning designs into layouts

From screenshots or design files, the assistant produces semantic HTML and modern CSS using grid, flexbox and custom properties, responsive across all breakpoints and consistent with existing stylesheets.

Accessibility audit

The AI checks pages for heading structure, alt text, form labels, keyboard operability and contrast, interprets automated test results and fixes findings against WCAG success criteria.

Performance and Core Web Vitals

Lighthouse reports are analysed, images converted to modern formats with responsive sources, critical CSS extracted, scripts deferred and layout shifts eliminated in a targeted way.

CMS templates and extensions

The agent builds themes, blocks or plugins for WordPress, TYPO3 or a headless CMS, including content models, templates and protection against common security flaws. The system’s coding standards are respected.

Technical SEO

The AI adds structured data, meta tags, hreflang annotations, readable URLs and XML sitemaps, and checks internal linking and canonicals for consistency. Broken redirects and duplicate content are uncovered.

Forms and integrations

Contact and enquiry forms are built with validation, spam protection and server-side processing, alongside integrations with newsletter, CRM or booking systems via their APIs. Errors and timeouts are handled cleanly.

Possible hands-on projects

Static website with a generator

A multilingual company website built with a static site generator, structured data, a sitemap and automated deployment through a CI pipeline.

Accessibility checker

A small tool that automatically audits a list of URLs, sorts findings by severity and generates a readable HTML report.

Appointment booking widget

An embeddable widget that loads available slots from an API, accepts bookings and triggers confirmation emails. The widget is accessible and fully responsive on all devices.

Prerequisites

Solid programming skills in Python (or TypeScript as an alternative), basic knowledge of databases and REST APIs, and some experience with LLM APIs. Good knowledge of HTML, CSS and JavaScript plus some experience with Git, a CMS or a front-end framework.

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

Websites are subject to GDPR, ePrivacy rules and, in many cases, accessibility legislation under the European Accessibility Act: generated code must not embed tracking or external resources without consent and must be checked for accessibility. Also watch the licences of fonts, images and code snippets, and keep hosting and CMS credentials out of prompts.

Suggested dates

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

  • Thu, 26 November 2026 – Fri, 27 November 2026 Online Request
  • Thu, 10 December 2026 – Fri, 11 December 2026 Berlin Request
  • Thu, 17 December 2026 – Fri, 18 December 2026 Online Request
  • Wed, 13 January 2027 – Thu, 14 January 2027 Stuttgart Request
  • Wed, 27 January 2027 – Thu, 28 January 2027 Online Request
  • Thu, 4 February 2027 – Fri, 5 February 2027 Hanover 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.

Can AI build an entire website for me?

For prototypes and simple sites, yes – and remarkably quickly. Production websites, however, need structure: clean components, verified accessibility, privacy compliance, performance and a well-designed deployment. On the course you learn to move AI-generated output into a maintainable project structure and safeguard it systematically.

Is the course suitable for career changers?

Yes, provided you have a grounding in HTML, CSS and JavaScript. AI explains code and concepts clearly and helps close knowledge gaps quickly. We make sure you understand the generated solutions and can evaluate them independently, rather than simply accepting whatever the assistant produces.

How much does “RAG & LLM Applications for Web 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.

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