RAG (Retrieval-Augmented Generation)
Retrieval-Augmented Generation (RAG) is a pattern where the system searches a document store, then asks an LLM to answer using the retrieved chunks — ideally with citations.
In depth
RAG exists because models do not know your PDFs, tickets, or policies. Instead of fine-tuning, you embed chunks, retrieve the nearest ones, and put them in the prompt.
MVP pieces: ingest, chunk, embed, retrieve with tenant filters, generate, cite, delete.
RAG is the wrong v1 if users will not upload documents or the answer is a 10-row FAQ.
Security: never retrieve another tenant’s chunks. That is the #1 production bug in AI apps.
Real example
A founder uploads 40 policy PDFs. Support asks “what is the refund window?” The system retrieves three chunks and answers with page citations.
Tools & calculators
Related terms
Embeddings
Embeddings are numeric vectors that represent text (or images) so similar meanings sit close together. RAG uses them to find relevant chunks.
AI Agent
An AI agent is a system where a model chooses actions (tools) in a loop until a job is done or a budget is hit — not a single chat completion.
AI SaaS
AI SaaS is multi-tenant software whose core paid job is performed by a model API, with accounts, plans, and a usage cap.
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