--- type: "LandingPage" title: "RAG Development: AI Answers with Sources" description: "RAG development from Berlin: AI answers with sources from your company knowledge – with a vector database, hybrid search, quality metrics and EU hosting." resource: "https://www.contextstudios.ai/rag-development" language: "en" tags: ["RAG development", "retrieval-augmented generation", "RAG system", "vector database", "RAG pipeline", "semantic search", "hybrid search", "knowledge AI", "document QA", "enterprise RAG"] generated: by: "process:contextstudios-md/1" at: "2026-10-08T20:44:11.533Z" status: "stable" --- # RAG Development: AI Answers with Sources RAG development connects language models with your company knowledge: for every question, the system finds the relevant documents and formulates an answer with source references. Context Studios, an AI-native development studio in Berlin, builds such systems with vector databases, hybrid search and quality measurement – GDPR-compliant and connected to your existing systems. Retrieval-augmented generation connects language models with your company knowledge for fact-based AI answers. Context Studios implements RAG with optimised embedding pipelines, semantic search and LLM orchestration. Source grounding reduces hallucinations significantly. Every system is tailored to your data — for reliable, knowledge-based AI. Retrieval-augmented generation (RAG) is an AI architecture in which a language model retrieves relevant passages from your own documents before every answer. The model answers on the basis of these sources and cites them. This makes answers more current, verifiable and far less prone to hallucinations – without retraining the model. Entity: RAG Development Specialisation: Vector databases, embedding strategies, hybrid search, re-ranking Technologies: Pinecone, Weaviate, pgvector, Convex, Cohere Rerank Target group: Companies with extensive knowledge bases and document collections Project duration: Typically 4–12 weeks for production-ready RAG systems Compliance: GDPR, data localisation, access control, audit trails ## What does a RAG system include? From document preparation to a production-ready knowledge base ### Intelligent document preparation Optimised embedding pipelines: documents, knowledge bases and FAQ collections are prepared and embedded precisely — with embedding models that suit your data. ### Vector database architecture Selection and setup of the right vector database — such as Pinecone, Weaviate, pgvector or Convex — with indexes that respond quickly even with large data volumes. ### Semantic search & hybrid retrieval Hybrid retrieval: a combination of semantic vector search and classic keyword matching to maximise recall and precision at the same time. ### Chunking strategy & embedding optimisation Intelligent document splitting for optimal context windows — with the right balance between granularity and context. ### Answer evaluation & quality assurance Automatic evaluation of answer quality, relevance and completeness. Every answer references the underlying documents and remains traceable. ### Incremental data updates Automatic indexing: new and changed documents are continuously embedded and made searchable — your knowledge base stays up to date. ## How is a RAG system built? ### Consultation Free 30-minute initial call via video. We get to know your knowledge sources and typical questions, identify suitable use cases and give you a first assessment of feasibility and timeline. ### Proposal & planning You receive a written proposal with scope, timeline and fixed price – including data sources, a permissions concept and an evaluation plan. ### AI-accelerated development Agile development with weekly demos. Goal: a working MVP in about 4 weeks, with production-ready code and automated tests. ### Launch & support Production deployment with complete documentation and 30 days of free bug fixing from final delivery. Maintenance and further development by agreement. ## Frequently asked questions about RAG systems Q: What is the difference between RAG and fine-tuning? A: RAG combines search with text generation: first the relevant documents are found, then the language model formulates an answer based on these sources. Fine-tuning, by contrast, adapts the model itself, for example to a style or technical vocabulary. RAG is particularly suitable when knowledge changes frequently and answers must be verifiable; both approaches can also be combined. Q: Which document formats does a RAG system support? A: A RAG system processes all common formats: PDF, Word, HTML, Markdown, emails, presentations and structured data from databases or spreadsheets. Content from Confluence, SharePoint, Google Drive or ticketing systems can also be connected via interfaces. We make scanned documents searchable with text recognition and prepare tables and charts separately so that no information is lost. Q: How many documents can a RAG system process? A: Very large collections too: modern vector databases search large numbers of text passages quickly, and optimised indexes keep response times stable as the collection grows. What matters most for quality is not volume but preparation – sensible passages, good metadata and up-to-date content. That is why we often start with the most important sources and expand step by step. Q: How current is the data in a RAG system? A: New and changed documents are indexed automatically and are then available for answers; deleted documents disappear from the index. We agree with you how often updates run – continuously via webhook, hourly or nightly. With every answer, users see the date of the source, so outdated information stands out immediately. Q: How do RAG systems prevent hallucinations? A: Through source grounding: the system answers only on the basis of the documents it finds and cites the sources. If it finds no suitable passage, it says so openly instead of guessing. In addition, we automatically check answers for consistency with the sources. This reduces hallucinations significantly and makes every answer verifiable for your employees. Q: What does a RAG system cost to run? A: Running costs depend on data volume, request volume, the chosen model and hosting; API costs depend on the model and volume. With caching and suitable embedding models we keep them predictable. For development: fixed price after scoping, proposal within 48 hours. A good way to start is a fixed-price workshop in which we prioritise data sources and use cases. Q: Can existing search systems be replaced by RAG? A: Often yes, or RAG complements them. Classic keyword search returns lists of hits, while a RAG system answers the question directly and points to the relevant passages. We often combine both in a hybrid search. Via APIs we connect the system to your CRM, helpdesk, intranet or chat tools, so your teams can use it where they already work. Q: Which vector database do you recommend? A: That depends on data volume, hosting requirements and budget. We use Pinecone, Weaviate, pgvector and Convex, among others. pgvector is a good fit if you already use PostgreSQL; specialised services pay off for very large collections or complex filters. We recommend the right solution after scoping, with reasons based on your requirements. Q: How do we measure the quality of a RAG system? A: Through automatic evaluation: we measure the relevance of the retrieved sources as well as the precision and completeness of the answers with test questions from your daily work, for example with tools such as ragas. We also collect user feedback in live operation. This makes quality measurable, and changes to chunking, search or prompts can be assessed in a targeted way. Q: Does our data stay under our control with RAG? A: Yes. We rely on GDPR-compliant hosting in the EU, access controls and encrypted storage. The system respects existing permissions, so employees only receive answers from documents they are allowed to see anyway. On request, we run the vector database and language model entirely in your own infrastructure, without external providers. ## RAG systems for your company Make your company knowledge intelligently searchable. Discuss your RAG project in a 30-minute call directly with the founder. ## Our RAG technology stack ## RAG solutions by industry ## Example projects Examples we can build for you ## RAG development — consultation in Berlin