RAG – how to build your own ChatGPT on company data
In short
RAG (Retrieval-Augmented Generation) explained. Vector database, document chunking, embeddings. LangChain, OpenAI API. Data security and costs.
- First name the problem and the goal.
- Then outline a simple step-by-step plan.
- Each step needs an owner and a deadline.
- Track results — without numbers it stays opinion.
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.
FAQ
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.
How I can implement this for you
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