RAG Developer.

Makes your enterprise knowledge searchable and usable for AI.

Remote & on-site DACH-wide
RAG Developer — enquire

A RAG developer builds systems for Retrieval-Augmented Generation: language models that look things up in your documents, wikis and databases before answering — and back up their answers with sources. This turns scattered enterprise knowledge into a system you can query reliably, without having to train a model on your data.

The craft lies not in the language model, but in the pipeline ahead of it: document preparation, chunking, embedding strategy, search and re-ranking determine the answer quality. This is precisely where solid engineering separates itself from demo quality.

01

Internal knowledge Q&A

Employees ask in natural language about policies, projects or products — with sourced answers from SharePoint, Confluence or file shares.

02

Support assistance

First-level support receives suggested answers from manuals and ticket history, with source references for quick verification.

03

Contract and document research

Legal or technical document holdings become precisely queryable — including version states and access rights.

04

RAG quality rescue

An existing RAG system delivers weak answers. Systematic diagnosis of retrieval quality, chunking and prompting instead of starting over.

Core competencies
Retrieval architectureChunking strategiesEmbedding modelsEvaluation (RAGAS and similar)Access-rights concepts
Tools & frameworks
Vector databases (Qdrant, pgvector, Weaviate)Elasticsearch/Hybrid SearchLlamaIndexRe-rankersUnstructured/parsers
Plus factors
SharePoint/M365 integrationGDPR-compliant architecturesMultilingual corpora
How do you tell whether poor answers stem from retrieval or from the model?
The core competency of the role: anyone who does not systematically separate the search problem from the generation problem is optimising blind.
How do you handle document access rights in RAG?
The most common enterprise pitfall: without rights-aware retrieval, employees see content they should never be allowed to see.
Which chunking strategy do you choose for contracts vs. wiki articles?
Practitioners answer with nuance by document type; theorists name a standard chunk size.
How do you evaluate RAG quality before go-live?
Robust systems need an eval dataset of real user questions with expected sources — not sample demos.
What does a freelance RAG developer cost?
According to our market observation, hourly rates in the DACH region in 2026 range between 100 and 150 euros, depending on enterprise experience with access-rights management and data quality.
What is RAG — and why not simply train the model on our data?
RAG lets the language model look things up in your documents before answering. This is more current, more cost-effective and more transparent than training: new documents take effect immediately, answers carry source references, and your data does not alter any model.
Does RAG work with our SharePoint and Confluence content?
Yes — common sources such as SharePoint, Confluence, file shares or ticketing systems can be connected. The decisive factor is the preparation: data quality and access rights determine the outcome more than the choice of model.
How long does a RAG project typically take?
A focused pilot with a single document source can be tested in production within four to eight weeks; expansion to further sources and access-rights models follows iteratively.

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