01/Glossary · AI & Automation
RAG — Retrieval-Augmented Generation
Technique that combines an LLM (GPT, Claude) with your private knowledge base so it answers with your data, not its general training.
02/Full definition
RAG (Retrieval-Augmented Generation) is the most-used architecture for building conversational bots with proprietary knowledge. It works in two steps: (1) when a question comes in, it searches your database (PDFs, FAQs, manuals converted to vector embeddings) for the most relevant fragments. (2) It passes those fragments to the LLM along with the question and asks it to answer based only on that. Result: the bot gives precise answers from your information, not hallucinations.
03/In Costa Rica context
In Costa Rica RAG is used for WhatsApp/web bots that answer technical product questions, support FAQs, and lead qualification. Typical cost: USD 1,500–3,000 for a full RAG bot (doc loading + embeddings + WhatsApp integration). Operational costs: USD 5–50/mo in LLM tokens plus USD 0–25/mo in vector database (Supabase, Pinecone).
04/Related reading on the site
05/Related terms
06/Frequently asked questions
Frequently asked questions
Is RAG cheaper than fine-tuning?▾
Yes, much cheaper. Fine-tuning requires training a custom model (USD 1,000+ per iteration, becomes obsolete when data changes). RAG just updates the knowledge base (USD 0 per update). 95% of use cases are better solved with RAG.
Ready to get a quote?
4 questions, 30 seconds. We give you the USD range + WhatsApp with your scope pre-filled.