RAG Workshop · 3 days

From 0 to your first RAG prototype

Three days in which you don't just understand RAG but build it yourself: evolving from the naive pipeline to a modular architecture, every step motivated by a concrete problem and quantitatively validated.

Duration:3 days
Participants:Max. 10 people
Preparation:Practice dataset included
The Problem

Between a RAG prototype and a production system lies a field of technical decisions that influence one another. Generic defaults don't work. This workshop makes the difference transparent and measurable, until you master it yourself.

01 - THE STRUCTURE

Three days, one common thread

From the first working prototype to advanced architecture, each day builds on the previous one.

1

Day 1

Naive RAG: foundation and its limits

You build your first working RAG pipeline and understand systematically why it isn't yet enough for production.

  • The full RAG path: from question to sourced answer
  • The building blocks of a pipeline and how they interact
  • A first working pipeline on the practice dataset
  • Failure modes: where naive RAG systematically breaks down
2

Day 2

Retrieval quality: the first upgrades

Every upgrade is motivated by a concrete problem and quantitatively validated on a shared test set.

  • Hybrid search (BM25 + vector) and reranking
  • Metadata filtering and chunking strategies
  • Contextual retrieval
  • Quantitative evaluation with Ragas
3

Day 3

Going deeper & advanced architectures

You learn to weigh the levers that determine quality, cost, and operations, and assess future RAG developments on your own.

  • Embeddings: selection and impact
  • The vector-DB landscape compared
  • Advanced RAG architectures
  • Production operations: running, monitoring, scaling

02 - THE METHOD

Why this isn't a webinar

Engineering instead of slides: you work on a real pipeline and measure every bit of progress.

Evolutionary, not theory

A step-by-step build from naive to modular RAG. You see each architecture emerge instead of consuming finished answers.

Problem-driven

Every upgrade is motivated by a concrete problem, no feature without a reason.

Quantitatively validated

Every improvement is measured on a shared test set (Ragas). You learn to prove, not to guess.

Hands-on

Throughout on a realistic practice dataset, transferable to your own documents.

03 - INDEPENDENCE

Which tool is right, you decide for yourself.

You learn to make well-founded decisions: build vs. buy and which stack fits which case. We weigh the Microsoft ecosystem, Copilot, Azure AI Search, Fabric, just as neutrally as open-source and best-of-breed stacks like Vespa.ai, Qdrant, pgvector, and Chroma.

By the end, you can judge for yourself what your project needs, from a ready-made platform to a custom build.

04 - YOUR OUTCOME

What you take away

After three days you can independently design, implement, and evaluate a production-ready RAG pipeline, and assess future RAG developments yourself.

  • A RAG pipeline you built and evaluated yourself
  • A decision framework for build vs. buy and stack selection
  • A quantitative evaluation methodology (Ragas) for your own projects
  • The ability to classify new RAG techniques independently

Production-ready RAG expertise in your team

05 - WHO IT'S FOR

Who is the workshop for?

Technical leads & architects

Plan RAG pipelines or make existing prototypes production-ready.

Product owners

Understand which levers influence answer quality.

IT decision-makers & CTOs

Make build-vs-buy and cloud-vs-on-prem trade-offs with confidence and plan budgets.

06 - THE WORKSHOP LADDER

Two ways to learn RAG

Start self-guided or go deep in the guided 3-day workshop.

Free

Self-Guided RAG Workshop

A free entry point to work through yourself.

Learn more
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RAG Workshop · 3 days

The guided deep dive from naive to modular RAG.

Kostenloser Self-Guided Workshop

Self-Guided RAG Workshop: Ihre RAG-Architektur Schritt für Schritt

Arbeiten Sie die wichtigsten Architekturentscheidungen für Ihre RAG-Pipeline in Ihrem eigenen Tempo durch: Strukturiert, praxisnah und direkt anwendbar.

  • Chunking-Strategie für Ihre Dokumenttypen
  • Retrieval-Architektur: Vektor, Hybrid oder Knowledge Graph?
  • Reranking: Qualität der Ergebnisse verbessern
  • Modellauswahl: LLM, Embedding & Reranker
  • Betriebsmodell: Cloud, On-Premise oder Hybrid?

Workshop anfordern

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Ready to master RAG yourself?

Book your free 30-minute consultation. We'll clarify your starting point and give you an honest assessment.

Let’s talk about how AI can move your business forward.

Get in touch, we look forward to your project.

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