RAG (retrieval-augmented generation)What it is, and why it's in our toolbox
Grounding model answers on your own corpus: embeddings, vector search, citations — the architecture that turns 'chatbot' into product.
Why Chimbuk uses it
Every AI assistant pitch we accept starts as a RAG design: chunk → embed → retrieve → answer with receipts. pgvector or Firestore vectors, per your existing stack.
How we work with it
The working rule on every engagement: a technology stays only while it pays rent — in developer speed, runtime performance, hosting cost or hiring availability for your future team. RAG (retrieval-augmented generation) passed that test; the written case for it (and its honest trade-offs) goes into your project's decision log, so nobody has to reverse-engineer the reasoning after handover.
In this studio today
You can see rag (retrieval-augmented generation) doing real work on this very site and across the Concept Lab demos — including the CSS-rendered device screens on /concepts — and, where it concerns shipped product, in the verified work ledger under our own Google Play label.
Begin the ascent
Want RAG (retrieval-augmented generation) doing this kind of work for you?
Tell us what you're building — we'll say honestly whether this tool (or a duller one) wins.