Training course · RAG & LLM Applications · React & Front End

RAG & LLM Applications for React and Front-End 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 React and Front-End Developers” is designed for roles such as React developers in product teams and agencies, Front-end engineers working with Next.js, Vite or similar frameworks and Maintainers of design systems and component libraries and shows step by step how to use RAG & LLM Applications productively, safely and transparently in React & Front End – 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.

Front-end development today means component architectures, state management, server components, design systems, accessibility and performance budgets – often all at once. AI coding assistants and agents speed up exactly the work that eats time: deriving components from designs, converting class components to hooks, writing Storybook stories and Testing Library tests, fixing ARIA issues or tracking down unnecessary re-renders. Because front-end code thrives on conventions, context is everything: design tokens, folder structure, styling approach and preferred libraries must be known to the assistant. This course shows you how to set those guardrails and how to verify results reliably, both visually and technically.

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?

  • React developers in product teams and agencies
  • Front-end engineers working with Next.js, Vite or similar frameworks
  • Maintainers of design systems and component libraries
  • Full-stack developers with a front-end focus
  • UI engineers responsible for accessibility and performance

Typical challenges we address

  • Inconsistent components and styles despite an existing design system
  • Hard-to-trace re-renders, bloated bundles and weak Core Web Vitals
  • Accessibility gaps and growing pressure from legal requirements
  • Legacy code with class components, outdated patterns and missing tests

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.
  • Have AI agents generate components, stories and tests that match your design system.
  • Use AI deliberately to improve accessibility, performance and legacy code in a systematic way.
  • Check generated front-end code visually, functionally and for maintainability before it ships.

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: React & Front End
  • Building components from designs
  • Testing Library tests
  • Auditing and improving accessibility
  • Modernising legacy code
  • Optimising performance
  • Forms and data fetching
Module 7 Data protection, quality & transfer to daily work
  • Under the European Accessibility Act and its national implementations, many digital services must be accessible, so generated UI code needs proper checks. Also watch the licences of reused components and icons, keep secrets out of the client bundle and make sure tracking and consent logic complies with GDPR and ePrivacy rules.
  • 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

Building components from designs

From screenshots, design specs or Figma exports, the assistant produces typed React components that use your existing design tokens and libraries, and adds Storybook stories covering every state.

Testing Library tests

The AI writes user-centred component tests with React Testing Library and Vitest or Jest, simulates interactions, mocks API calls and covers loading, error and empty states.

Auditing and improving accessibility

The agent reviews components for semantic HTML, ARIA attributes, focus management and contrast, interprets axe results and fixes findings transparently against WCAG success criteria. Keyboard and screen reader behaviour is covered too.

Modernising legacy code

Class components are converted to function components with hooks, prop drilling is replaced by context or a proper state management approach and PropTypes give way to TypeScript types – with tests as a safety net.

Optimising performance

The AI pinpoints unnecessary re-renders, proposes memoisation, code splitting and lazy loading, analyses bundle reports and explains how each measure affects metrics such as LCP and INP.

Forms and data fetching

Complex forms with validation schemas, error messages and optimistic updates are generated alongside data-fetching logic with caching, retries and clean handling of loading and error states.

Possible hands-on projects

Interactive KPI dashboard

A React dashboard with charts, filters and a responsive layout that loads data from a REST API, built entirely by an AI agent from a short specification.

Component library with Storybook

A small, accessible component library with buttons, dialogs and form fields, including design tokens, Storybook documentation and automated tests. It forms the basis for consistent interfaces across several projects.

Accessibility checker for pull requests

A script that runs axe against Storybook stories automatically and turns the findings into a clear report attached to pull 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. Hands-on experience with React and modern JavaScript or TypeScript, plus a working knowledge of CSS, npm and Git.

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

Under the European Accessibility Act and its national implementations, many digital services must be accessible, so generated UI code needs proper checks. Also watch the licences of reused components and icons, keep secrets out of the client bundle and make sure tracking and consent logic complies with GDPR and ePrivacy rules.

Suggested dates

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

  • Wed, 25 November 2026 – Thu, 26 November 2026 Online Request
  • Thu, 10 December 2026 – Fri, 11 December 2026 Salzburg Request
  • Thu, 17 December 2026 – Fri, 18 December 2026 Online Request
  • Tue, 19 January 2027 – Wed, 20 January 2027 Bremen Request
  • Mon, 1 February 2027 – Tue, 2 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.

Can AI implement designs pixel-perfectly?

AI assistants now translate layouts from screenshots or specs remarkably well, especially when they know your design tokens and components. Pixel-perfect results still come from iteration, though. On the course you learn to feed back visual deviations precisely and to use Storybook and visual tests as your control mechanism.

Does the framework I use matter?

The course centres on React, but the working methods transfer to Next.js, Remix or Vite-based setups. We show how to capture framework-specific rules such as server and client components or routing conventions in instruction files, so the assistant produces code that fits your stack.

How much does “RAG & LLM Applications for React and Front-End 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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