RAG – how to build your own ChatGPT on company data
RAG (Retrieval-Augmented Generation) explained. Vector database, document chunking, embeddings. LangChain, OpenAI API. Data security and costs.
Imagine an AI assistant that knows every document in your company. Policies, procedures, offers, contracts. Answers questions in seconds, citing sources. That is RAG.
What is RAG? 🤖
RAG combines information retrieval with language model generation. You ask a question → the system searches your documents → finds relevant fragments → the AI answers based ONLY on those fragments. No hallucinations, no guessing – answers grounded in your data.
⛔ MYTH: "RAG is only for corporations with AI budgets"
FACT: In 2026, even a solo founder can deploy RAG. Free open-source tools (LangChain, Chroma, Llama), cheap embedding APIs ($5/month) and basic Python knowledge. Even a paid solution costs $100-150/month – less than one hour of a developer's time searching through documentation.
Check our AI services or contact me for a RAG consultation.
Frequently Asked Questions
- Is RAG safe for business data?
- Yes. In RAG you can host everything locally or on a private server. Data never leaves your infrastructure. You only send embeddings (not documents) to the AI model, or use local models like Llama entirely on-premise.
Michał Grycz
Software engineer and founder of MGSupport. Designs and builds high-performance web systems, e-commerce, and business automations.