Training course · Lovable · AI Engineering & LLM Applications

Lovable for AI Engineers

Build web apps from prompts: take Lovable from idea to a working React app with Supabase backend, GitHub sync and deployment.

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

This course connects Lovable with the everyday processes of AI Engineering & LLM Applications. Participants work on realistic scenarios and leave with prompts, workflows and checklists they can use straight away.

Lovable turns plain-language descriptions into complete web applications built on React, TypeScript, Tailwind CSS and Vite. In this one-day course you learn how to phrase requirements so that Lovable produces clean, maintainable code, how to connect a database, authentication and storage through the Supabase integration, and how to steer changes iteratively instead of starting over every time something breaks. The focus is on real workflows: from the first prompt through data model and permission design to a published application running on your own domain.

For developers, Lovable becomes genuinely useful when the generated code does not stay locked inside the tool. You therefore work extensively with the two-way GitHub sync, review the generated code in your own editor, add tests and move the project into your existing toolchain where needed. You also get a clear view of the limits: Row Level Security, Edge Functions, secrets handling, performance and the maintainability of larger codebases. That lets you judge when Lovable is the fastest route and when conventional development or an agentic coding tool is the better fit.

AI engineering is the discipline of turning language models into dependable products: chat assistants, document analysis, retrieval-augmented generation, agents with tool access and AI features inside existing applications. The code around the model is often more demanding than the model call itself, because prompt versioning, structured output, evaluation, cost control, latency, observability and protection against prompt injection decide success in production. AI coding assistants and agents accelerate exactly this work, from building evaluation pipelines and tool definitions to MCP servers and root-cause analysis in traces. In this course you learn to develop LLM applications systematically with AI support, make their quality measurable and run them securely, cost-effectively and in compliance with regulation.

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?

  • Software developers integrating LLM features into products
  • AI engineers and machine learning engineers
  • Data scientists focusing on generative AI
  • Backend and platform teams providing AI infrastructure
  • Technical product owners for AI features

Typical challenges we address

  • Prototypes shine in demos but deliver inconsistent quality in production
  • Without systematic evaluation, changes to prompts and models are risky
  • Costs, latency and rate limits spiral as usage grows
  • New security risks from prompt injection, data leakage and over-privileged agents

What you will be able to do

  • You write prompts that lead Lovable to produce well-structured, maintainable React applications.
  • You connect Supabase for database, authentication and storage and verify Row Level Security.
  • You iterate on the design deliberately without breaking parts of the app that already work.
  • You sync projects with GitHub and continue development in your usual coding environment.
  • Build RAG applications, agents and LLM features with AI coding tools systematically and to production standard.
  • Make the quality of LLM applications measurable and manageable with automated evaluations.
  • Run LLM applications securely and cost-effectively, with observability, prompt injection defences and clear permissions.

Course outline

Module 1 Fundamentals and prompting strategy
  • How Lovable works: the generated stack of React, TypeScript, Tailwind CSS and shadcn/ui
  • Describing requirements in a structured way: audience, pages, data objects, roles
  • Chat mode versus direct edits: when to plan and when to build
  • Using the knowledge base and project instructions for consistent output
Module 2 Backend with Supabase
  • Connecting a Supabase project and deriving tables from your prompt
  • Setting up authentication with email, magic links and OAuth providers
  • Understanding, reviewing and tightening Row Level Security policies
  • Using Edge Functions, storage buckets and secrets for external APIs
Module 3 Design iteration and UI quality
  • Visual editing of individual elements and targeted layout prompts
  • Working from screenshots and Figma designs
  • Ensuring responsive behaviour, accessibility and design-system consistency
  • Debugging with console output and reverting to earlier versions
Module 4 GitHub sync and a professional workflow
  • Setting up two-way synchronisation with a GitHub repository
  • Continuing work locally in VS Code, Cursor or Claude Code
  • Introducing branches, code review and tests for generated code
  • Keeping dependencies, environment variables and project structure tidy
Module 5 Deployment, operations and limits
  • Publishing with Lovable hosting and a custom domain
  • Alternative deployments via Vercel, Netlify or your own infrastructure
  • Realistically assessing credit usage, project size and common dead ends
  • Security and data-protection review before go-live, including GDPR questions
Module 6 Practice lab: AI Engineering & LLM Applications
  • Building RAG pipelines
  • Automating evaluation
  • Developing agents and tools
  • Securing structured output
  • Observability and cost
  • Defending against prompt injection
Module 7 Data protection, quality & transfer to daily work
  • LLM applications often process personal and confidential data: you learn to assess EU AI Act requirements on transparency and risk classes, design GDPR-compliant data flows with processing agreements and deletion policies, manage API keys securely and respect the licence terms of models and datasets.
  • 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 RAG pipelines

Implement ingestion, chunking, embeddings, vector search, hybrid retrieval and reranking, and generate answers with citations while honouring the access rights of source documents at retrieval time.

Automating evaluation

Create test sets with expected answers, define metrics for relevance, faithfulness and format, calibrate LLM-as-a-judge scoring and run evaluations in CI on every prompt or model change.

Developing agents and tools

Design tool definitions and MCP servers, implement agent loops with planning, tool calls and stop conditions, and build in permissions and human approval for consequential actions.

Securing structured output

Define JSON schemas for model responses, validate output, implement retry strategies on failure and make extraction tasks such as invoice or contract data robust. Faulty responses never reach downstream systems.

Observability and cost

Capture traces, token usage, latency and error rates, analyse problematic conversations and cut costs through caching, model routing and context reduction. Budgets and alerts per feature prevent surprises.

Defending against prompt injection

Test attack scenarios via manipulated documents, web pages or tool results, filter inputs and outputs, limit agent permissions and make red-teaming tests a fixed part of the pipeline.

Possible hands-on projects

Knowledge assistant with sources

A RAG chat over internal documents with citations, feedback buttons and an evaluation page that tracks answer quality across test questions.

Evaluation harness

A command-line tool that runs prompt variants against a test set, scores the results and clearly shows differences between versions.

Document extractor

A service that accepts PDFs, extracts structured data against a JSON schema, validates it and flags uncertain cases for manual review.

Prerequisites

Basic web development knowledge (HTML, CSS, JavaScript) and some experience with Git are helpful. Prior React or Supabase experience is not required. You have solid programming skills, ideally in Python or TypeScript, and experience with APIs; prior exposure to LLM APIs helps but is not required.

Technical requirements

A laptop with an up-to-date browser, a Lovable account (a paid plan is recommended for intensive use) and free GitHub and Supabase accounts.

Data protection & compliance

LLM applications often process personal and confidential data: you learn to assess EU AI Act requirements on transparency and risk classes, design GDPR-compliant data flows with processing agreements and deletion policies, manage API keys securely and respect the licence terms of models and datasets.

Suggested dates

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

  • Wed, 25 November 2026 Online Request
  • Thu, 10 December 2026 Bremen Request
  • Fri, 15 January 2027 Online Request
  • Wed, 20 January 2027 Salzburg Request
  • Mon, 25 January 2027 Online Request
  • Thu, 11 February 2027 Kaiserslautern Request

Frequently asked questions

Do I own the code Lovable generates?

Yes. The output is standard React and TypeScript code that you can push to your own repository via the GitHub sync. From there it can be developed, tested and deployed entirely independently of Lovable. In the course we show you how to set this up properly from day one so you avoid any lock-in.

Is Lovable suitable for production applications?

For prototypes, MVPs, internal tools and modest customer-facing apps, yes, provided you review security and the data model carefully. Supabase Row Level Security, secrets handling and the maintainability of a growing codebase all need attention. The course covers exactly which checks make sense before you go live.

How does Lovable differ from Cursor or Claude Code?

Lovable is a browser-based app builder with a fixed stack, built-in hosting and Supabase integration, which makes it ideal for getting started quickly. Cursor and Claude Code work inside any existing codebase and technology. Many teams combine them: start in Lovable, then continue in the editor via GitHub.

How does AI engineering differ from classic machine learning?

In classic machine learning you train your own models on your own data. In AI engineering you usually work with pre-trained language models via APIs or self-hosted models and focus on context, retrieval, tools, evaluation and operations. Statistics and training know-how matter less, while software engineering skills matter all the more. That is precisely where this course comes in.

How do we know whether our LLM application is good enough?

With an evaluation set of realistic questions and expected results, a mix of rule-based checks, LLM-as-a-judge and human spot checks, plus monitoring in production. The key is running evaluations automatically on every change. In the course you build such a setup and learn to calibrate judge models.

How much does “Lovable for AI Engineers” 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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