Training course · RAG & LLM Applications · JavaScript & TypeScript

RAG & LLM Applications for JavaScript and TypeScript 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

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

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.

JavaScript and TypeScript now run in the browser, on servers, in build pipelines and on edge platforms. That breadth comes with complex toolchains: bundlers, transpilers, linters, package managers and an npm ecosystem that never stands still. AI coding assistants and agents remove a great deal of that friction. They migrate JavaScript modules to TypeScript incrementally, derive precise types from API responses, fix ESLint findings, write Vitest or Jest tests and explain nested generics in plain language. For the output to fit your project, the tools need clear guardrails: tsconfig strictness, module system, coding guidelines and test commands. This course shows you how to provide that context deliberately and how to scrutinise generated code.

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?

  • Front-end and full-stack developers working in JavaScript or TypeScript
  • Teams migrating an existing JavaScript codebase to TypeScript
  • Library and SDK maintainers in the npm ecosystem
  • Tech leads who own tooling, linting and build processes

Typical challenges we address

  • Gradual type erosion through any, type assertions and lax tsconfig settings
  • Complex toolchains mixing bundlers, transpilers, ESM and CommonJS that are hard to debug
  • Huge dependency trees carrying security and maintenance risk
  • Asynchronous code with hard-to-reproduce race conditions and gaps in error handling

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.
  • Use AI agents deliberately for TypeScript migrations, typing work and test generation.
  • Feed assistants your tsconfig, lint rules and project conventions so generated code fits your existing stack.
  • Critically assess proposed npm dependencies and generated code for type safety and security.

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: JavaScript & TypeScript
  • Incremental TypeScript migration
  • Deriving types from APIs and schemas
  • Writing Vitest and Jest tests
  • Fixing ESLint findings and code smells
  • Debugging build and bundler issues
  • Upgrading dependencies
Module 7 Data protection, quality & transfer to daily work
  • The npm ecosystem is a frequent target of supply-chain attacks, so vet every dependency the AI suggests for provenance, maintenance and licence (for example copyleft terms). Keep API keys and tokens out of prompts and clarify contractually whether your source code may be used to train the provider’s models.
  • 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

Incremental TypeScript migration

The agent converts JavaScript files to TypeScript module by module, infers types from usage, replaces any with precise types and tightens tsconfig strictness in controlled steps until the compiler runs clean.

Deriving types from APIs and schemas

JSON samples, OpenAPI specifications or database schemas are turned into TypeScript interfaces and Zod schemas for runtime validation, so front end and back end share one consistent contract.

Writing Vitest and Jest tests

For utilities, hooks and services, the AI generates unit tests with sensible mocks, fake timers and snapshot strategies, runs them and fixes failing cases on its own.

Fixing ESLint findings and code smells

The assistant works through lint warnings systematically, replaces outdated patterns such as callback chains with async/await and explains for each rule why the change makes sense.

Debugging build and bundler issues

Error output from Vite, webpack or esbuild is analysed, ESM/CommonJS conflicts are resolved and configuration for path aliases, tree shaking or source maps is corrected.

Upgrading dependencies

The AI reads changelogs for major npm package upgrades, adapts code to breaking changes, updates lockfiles and verifies everything through a type check and a full test run.

Possible hands-on projects

Type-safe API client

A small TypeScript package that generates a type-safe fetch client with Zod validation from an OpenAPI specification, ready to publish to npm with tests.

Browser extension

A TypeScript browser extension that analyses page content and copies it to the clipboard in a structured format – including manifest, content script and automated build.

Monorepo health check

A Node script that inspects workspaces in a monorepo, detects duplicate or inconsistent dependency versions and proposes a clean-up plan.

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 command of modern JavaScript, a working knowledge of TypeScript and experience with npm, pnpm or yarn 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

The npm ecosystem is a frequent target of supply-chain attacks, so vet every dependency the AI suggests for provenance, maintenance and licence (for example copyleft terms). Keep API keys and tokens out of prompts and clarify contractually whether your source code may be used to train the provider’s models.

Suggested dates

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

  • Tue, 24 November 2026 – Wed, 25 November 2026 Online Request
  • Mon, 30 November 2026 – Tue, 1 December 2026 Hanover Request
  • Wed, 16 December 2026 – Thu, 17 December 2026 Online Request
  • Thu, 14 January 2027 – Fri, 15 January 2027 Online Request
  • Mon, 25 January 2027 – Tue, 26 January 2027 Munich Request
  • Wed, 3 February 2027 – Thu, 4 February 2027 Online 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.

How well do AI assistants handle complex TypeScript types?

Current models understand generics, conditional types and mapped types very well and can explain them clearly. For highly complex type constructions, though, the compiler remains the ultimate judge. On the course you learn to have the agent run tsc iteratively, so suggestions are not merely plausible but genuinely type-correct before you accept them.

Is the course suitable for pure front-end developers?

Yes. The focus is the language and its tooling – typing, tests, linting, builds and dependencies – which matter equally on the front end and the back end. If you want to go deeper into React or Node.js specifically, our catalogue includes dedicated course variants for those stacks.

How much does “RAG & LLM Applications for JavaScript and TypeScript 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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