RAG Strategy for SMEs

What we actually do

1. RAG fundamentals: what retrieval-augmented generation actually means — and where it breaks in practice
2. Architecture components: embedding, vector DB, chunking, reranking, generation
3. Model choice: cloud (Voyage, OpenAI) vs. local (Sentence-Transformers) — cost/privacy/quality
4. Requirements workshop: derive needs from your own corpus
5. Vendor evaluation: how to assess RAG solutions neutrally (not by sales demo)
6. Hands-on: we build a mini-RAG live with your sample dataset
7. Measurement setup: how to know whether your RAG system is actually good

What you take away

• Clear understanding of which RAG architecture fits your use case
• First (skeleton) requirements document for vendor inquiries
• Evaluation protocol for fair RAG comparisons
• Realistic cost/effort estimate

Method

Full-day workshop with strong hands-on focus. We use our own production RAG platform RAG-Wissen as reference. You leave with code snippets, eval scripts, and requirements templates.

Prerequisites

Basic technical affinity (you do not need to code, but should understand architecture diagrams). Ideally, you have a rough idea of the data corpus you want to apply RAG to.