Glossary:Retrieval-Augmented Generation (RAG)
9 min.
What is Retrieval-Augmented Generation (RAG)?
Retrieval-Augmented Generation (RAG) is an AI technique where a language model (Large Language Model, LLM) actively queries an external knowledge source before generating a response. Instead of relying solely on trained parameters, the model first retrieves relevant documents, product pages, or database entries and uses these as a basis for its answer.
The result: more precise, factually accurate outputs based on actual content, not on estimates from training data.
40 million AI-powered sessions have already been processed by branchly, and each one runs through a RAG architecture that retrieves answers directly from the website content of the respective company (Source: branchly, 2026).
How does RAG work?
RAG combines two phases that occur within milliseconds:
Retrieval: The user's query is transformed into a search vector. The system searches a vector storage or an indexed document base and finds the most relevant passages.
Augmentation: The retrieved content is embedded as context in the prompt of the language model. The model thus "sees" the actual documents before it responds.
Generation: The LLM formulates a response in natural language based on the retrieved context, not on assumptions.
Grounding: Good RAG implementations transparently cite sources so that users can trace where information comes from.
At branchly, this process is deeply integrated into the branchlyAI engine. Every module, whether chatbot, AI search, advisor, or navigator, accesses the website content, product catalogs, and knowledge bases of the company via RAG. The AI does not invent answers; it reads what is actually on the website.
RAG vs. pure LLM (without Retrieval)
Feature | Pure LLM (without RAG) | LLM with RAG |
|---|---|---|
Source of Knowledge | Only training data (cutoff date) | Current external documents and databases |
Hallucination Rate | High for specific facts | 35–60% lower according to Wołk study (MDPI, 2025) |
Current Information | Static, outdated without retraining | Dynamic, always up-to-date with the knowledge base |
Company Knowledge | No access to internal data | Full access to selected data sources |
Source Citation | Not possible | Transparent citation of sources |
Compliance | Hard to audit | Answers traceable to original documents |
Implementation Cost | Fine-tuning ~310,000 $/year | RAG ~55,000 $/year (~80% cheaper) |
Update Effort | New training needed | Updating the knowledge base is sufficient |
The cost difference is significant: According to a TCO analysis by InSightEdge (October 2025), RAG operates at around 55,000 US dollars per year, while fine-tuning costs about 310,000 US dollars, a difference of nearly 80 percent (Source: InSightEdge, 2025).
Why RAG is crucial for corporate websites
Reducing hallucinations, gaining trust
AI systems without retrieval tend to generate convincingly sounding but false information. A peer-reviewed study by Wołk (MDPI Electronics, October 2025) shows that RAG reduces the hallucination rate by 35–60%. The best implementations achieve a hallucination rate of only 5.8% (Source: Wołk, MDPI Electronics, 2025).
For companies, this means: Visitors receive answers that match your actual products, prices, and policies, not what a model summarizes from the training internet.
Current knowledge without new training
As soon as you update a product, add a new FAQ, or change an offer, this is immediately reflected in RAG-supported answers. Fine-tuning, on the other hand, requires elaborate retraining processes, new data, and considerable budget. RAG separates knowledge from the model, making updates trivial.
Your company data as a competitive advantage
Public LLMs do not know your product portfolio. RAG gives your AI access to exactly what defines your company: product details, service descriptions, customer documents, industry-specific knowledge. This is the difference between a generic responder and a digital advisor that truly understands your company.
branchly consistently implements this principle: The branchlyAI engine bases all module responses on the customer's website content. The generative AI chatbot does not invent answers; it reads the website and summarizes what it actually says.
Data protection and compliance
RAG architectures allow granular control over which data the model sees. This is particularly relevant for European companies under the GDPR and the EU AI Act: You determine which documents flow into the retrieval index and retain control over sensitive content. Branchly operates all infrastructure on Microsoft Azure in European data centers.
Market Development: RAG is Growing Rapidly
The global RAG market is growing faster than almost any other AI segment. According to MarketsandMarkets (November 2025), the market is expected to grow from 1.94 billion US dollars in 2025 to 9.86 billion US dollars by 2030, with an annual growth rate (CAGR) of 38.4% (Source: MarketsandMarkets via GlobeNewswire, 2025).
Corporate practice confirms this trend: Databricks (November 2025) found that 70% of businesses employing generative AI are using RAG or retrieval tools. The use of vector databases, which are the central building block of modern RAG systems, has increased by 377% compared to the previous year (Source: Databricks State of AI, 2025).
RAG is no longer an experimental niche technology. It is the dominant method by which companies apply generative AI to their own data.
RAG in Practice: Use Cases
E-Commerce
A visitor asks: "Which camera is suitable for sports photography under 800 €?" A pure LLM would draw from the training internet and possibly recommend outdated models. With RAG, branchly searches the current product catalog of the store, filters by criteria, and provides a recommendation linked to the actual available product page. The advisor mode of branchly uses this architecture to combine product advice with retrieval, thus measurably increasing conversions.
Tourism
Tourismus Regensburg, one of the companies in the branchly client base, serves international guests in their native language, even though the website only exists in German. RAG makes this possible: The branchlyAI engine retrieves the German-language content, and the LLM generates a response in the visitor's language. Branchly supports 101 languages natively without the need for translated content. Over 11 million users have been served this way.
Financial Services
A financial service provider like IKB uses branchly to answer frequently asked questions about account models, fees, and processes. RAG ensures that the AI only cites from approved documents, does not mix information from the public internet, and that every response is traceable. Compliance teams appreciate the auditability: They can see which documents contributed to which responses. Sensitive inquiries are seamlessly forwarded to human advisors.
RAG and the branchly Platform
branchly is designed as a RAG-native system. The branchlyAI engine connects all six modules with the customer's website content level:
Module | RAG Application at branchly |
|---|---|
Chatbot (Generative AI Chatbot) | Responds from website content, FAQ databases, and product catalogs |
AI Search | Hybrid search combines vector rankings with classic relevance factors |
Advisor | Product recommendations based on live catalog data, not on training data |
Navigator | Step-by-step guidance accesses process documents and manuals |
Forms | Adaptive forms qualify leads based on retrieved product context |
Voice Agent | Voice-based responses, grounded in company data |
The result is interaction rates that far exceed the industry average: branchly widgets reach 5–10% of website visitors, compared to 0.5–1% for typical non-RAG-supported solutions. Implementations embedded solidly in a page achieve interaction rates of 45–50% (Source: branchly client data, 2026).
Entry prices start at 499 euros per month (Starter, including 1,000 sessions). Access to the RAG infrastructure, multilingual support, and the complete module stack is included in every plan.
Related Terms
Natural Language Processing (NLP)
AI Chatbot
AI Search
Conversational AI
Agentic RAG
Hybrid Search
Frequently Asked Questions
What is Retrieval-Augmented Generation in simple terms?
Retrieval-Augmented Generation (RAG) is a method where an AI first looks up external documents before responding. Instead of drawing from its own memory, the system reads relevant pages, products, or documents and summarizes what it finds. The result is answers based on actual content, not estimates.
What is the difference between RAG and Fine-Tuning?
Fine-Tuning permanently integrates new knowledge into the model parameters through extensive retraining. RAG dynamically connects the model with an external knowledge base without altering the model itself. RAG is significantly cheaper (around $55,000 instead of $310,000 per year according to InSightEdge 2025), easier to keep up-to-date, and allows granular control over what data the AI sees.
Why does a RAG-based AI hallucinate less?
Because the model does not have to guess. Instead of reconstructing an answer from training patterns, it generates based on a concrete document it has available in the current context. According to the Wołk study (MDPI Electronics, October 2025), RAG reduces hallucinations by 35–60%. The best implementations achieve a hallucination rate of just 5.8%.
How current are RAG answers?
As current as the knowledge base from which the system pulls. As soon as you update a product, a FAQ, or a price, it immediately reflects in the answers without retraining. This is one of the biggest practical advantages over pure LLMs with training data cut-off.
Is RAG suitable for small and medium-sized enterprises?
Yes. RAG scales from small knowledge databases to large product catalogs. branchly connects RAG with a simple onboarding experience: You connect the platform to your website, the branchlyAI engine indexes the content, and the system is ready to use within minutes. Technical infrastructure knowledge is not necessary for this.
What data can RAG retrieve?
RAG works with all structured and unstructured data that can be loaded into a search index: website pages, product catalogs, PDF documents, FAQ databases, knowledge articles, support tickets, CRM data, and more. At branchly, the focus is on website content and product data, which is exactly what visitors search for most often.
How does RAG differ from classic keyword search?
Classic keyword search finds documents that contain the exact words searched for. RAG understands the semantic meaning of a query, so it can also find documents that are thematically relevant without using the exact words. It then generates a coherent answer from the found content instead of just returning a list of links. branchly's hybrid search combines both approaches.
Can RAG work in multiple languages?
Yes. Modern embedding models and LLMs understand semantic similarities across languages. This means: A query in English can find documents in German, and the model responds in the user's language. branchly leverages this for its native support of 101 languages, without needing translated content in the retrieval index.
Is RAG GDPR compliant?
RAG itself is a technique, not software, so compliance depends on the specific implementation. The crucial factors are: where are the vector databases hosted? What data flows into the index? Are personal data processed? branchly operates the entire RAG infrastructure on Microsoft Azure in European data centers, is GDPR compliant, and EU-AI-Act ready. Each company retains control over which content is indexed.
How widespread is RAG in companies today?
Very widespread. According to Databricks (November 2025), 70% of companies using GenAI already utilize RAG or retrieval tools. The use of vector databases, the technical foundation of RAG systems, has increased by 377% compared to the previous year. RAG has established itself as the standard architecture for enterprise-specific AI applications.
