RAG Systems (Retrieval-Augmented Generation)
Build RAG systems that combine retrieval with LLM generation for accurate, context-aware AI
Large Language Models are powerful — but unreliable when they operate without context. RAG (Retrieval-Augmented Generation) solves this by grounding AI responses in your real data.
H-Studio designs and builds production-grade RAG systems that combine semantic retrieval with LLM generation to deliver accurate, explainable, and up-to-date AI outputs — without hallucinations.
This is how AI becomes usable in real products, operations, and enterprise systems.
What RAG Systems Are (and Why They Matter)
RAG systems connect LLMs with external knowledge sources:
Instead of guessing, the model retrieves relevant information first, then generates responses based on verified context. This enables:
What We Build with RAG
Knowledge-Grounded AI
Product & Customer Use Cases
RAG Architecture We Implement
Data Ingestion & Knowledge Modeling
We structure your data properly:
Everything is normalized, chunked, and indexed semantically.
Vector Search & Retrieval
We implement:
Retrieval quality determines generation quality.
LLM Integration & Prompt Engineering
We connect retrieval to generation:
The model responds only from retrieved context, not imagination.
Governance, Control & Monitoring
Production RAG requires control:
Typical RAG Use Cases
Who RAG Is For
Start with a RAG Architecture Review
We help you define: what data should be retrieved, how accuracy is enforced, and where RAG adds real value.
FAQ
What's the difference between RAG and fine-tuning?
Fine-tuning trains a model on your data, which is expensive, slow to update, and can't access real-time information. RAG retrieves relevant information at query time and uses it as context for generation. RAG is faster to deploy, easier to update, and can access live data sources.
How do you ensure RAG systems don't hallucinate?
We enforce strict constraints: the LLM only generates from retrieved context, we use confidence thresholds, we implement citation requirements, and we add fallback logic when retrieval quality is low. We also monitor outputs and log all generations for auditability.
What data sources can RAG systems use?
RAG can retrieve from documents (PDF, DOCX, HTML), databases, APIs, CRM/ERP systems, knowledge bases, wikis, and real-time data streams. We structure and index everything semantically so the system can find relevant information quickly.
How long does it take to build a RAG system?
A basic RAG system (data ingestion + retrieval + LLM integration) typically takes 6-10 weeks. Complex RAG with multiple data sources, advanced retrieval logic, and extensive governance can take 12-20 weeks. We start with an architecture review to define scope.
Can RAG systems work in German and English?
Yes — we build multilingual RAG systems that handle German, English, and other languages. We use multilingual embeddings, language-aware retrieval, and prompt engineering that respects language boundaries. RAG systems can answer in the language of the query.
Related Services
We provide RAG systems development services for businesses across Germany. Our Berlin-based team specializes in RAG architecture, retrieval-augmented generation, vector search, LLM integration, enterprise knowledge bases, and production-ready RAG systems.