Source 1
RAG workflow
AI/RAG basics
Retrieval-Augmented Generation starts with a user question, retrieves relevant knowledge snippets, and passes those snippets into an answer step so the response stays tied to available context.
basicragwork
Beginner prototype
This demo makes the RAG idea visible without overstating the build. It searches a tiny local knowledge base, ranks matching snippets, and drafts an answer from those sources. It does not use a real LLM, embeddings model, or vector database yet.
Local RAG-style demo
This is a beginner prototype: it retrieves matching snippets from local data, shows the sources, and drafts a grounded answer. No external model, API key, embeddings service, or production vector database is connected.
Draft answer
Keyword retrieval prototypeBased on the retrieved source snippets: Retrieval-Augmented Generation starts with a user question, retrieves relevant knowledge snippets, and passes those snippets into an answer step so the response stays tied to available context. A related retrieved note says: A useful RAG interface should show what information was retrieved. Visible sources make it easier to inspect the answer and notice when the assistant is missing context. This is a basic RAG-style flow because the answer is built from retrieved context instead of pretending to know everything.
Query tokens
basic, rag, assistant, work
Sources found
3
Model status
No external LLM
Source 1
AI/RAG basics
Retrieval-Augmented Generation starts with a user question, retrieves relevant knowledge snippets, and passes those snippets into an answer step so the response stays tied to available context.
Source 2
Prompt design basics
A useful RAG interface should show what information was retrieved. Visible sources make it easier to inspect the answer and notice when the assistant is missing context.
Source 3
AI learning path
A stronger RAG version would add document upload, chunking, embeddings, vector search, a server-side LLM call, citations, and evaluation checks for answer quality.