AI Data Retrieval, Explained
An LLM only knows what you put in front of it. Everything interesting in AI right now — RAG, vector databases, chunking — is really one problem wearing different costumes: how do you find the right data and hand it to the model at the right moment? This is the map.
There are two fundamentally different ways to retrieve data for AI. SQL finds facts. It's exact: you ask for a customer's balance and you get that customer's balance. Vector search finds meaning. It returns the passages that are semantically closest to your question, even if they don't share a single keyword.
RAG (Retrieval-Augmented Generation) is the architecture that wires retrieval to a language model: pull the relevant context, hand it to the model, and let it answer from your data instead of its training. Chunking is the unglamorous step that decides how well any of it works — split your documents wrong and even a perfect model retrieves garbage.
Most teams try to run one method where they need two, and then wonder why the AI keeps getting it wrong. The guides below walk through each piece in order.
The retrieval stack, piece by piece
Read in order, or jump to what you're stuck on.
RAG Explained
What Retrieval-Augmented Generation is and why it stops AI from guessing. Start here.
SQL vs. RAG
When to query for facts vs. retrieve for meaning — and why teams need both.
Vector Databases vs. SQL
How semantic search works, and why it searches by meaning, not exact match.
RAG Chunking Strategies
How to split documents into the right-sized pieces for a vector database.
Data Retrieval Deep Dive
Layering SQL and vector retrieval into one modern, AI-ready data stack.
Data Hygiene
Garbage in, hallucination out. Why retrieval quality starts with clean data.
Retrieval lives or dies on data quality. Check yours first.
Score my data free