Understanding Retrieval-Augmented Generation (RAG),
vector embeddings, and why "just searching for it" isn't enough
Large Language Models have a limited "context window" — like a desk that can only hold so many papers at a time.
Your Organization's Documents
We need to choose the right information to include
What if we could convert text into numbers that capture its meaning?
Each dot represents a chunk of text from your documents, plotted by its meaning. Points that are close together are semantically similar — even if they use completely different words.
By finding the nearest neighbors in this space, we retrieve documents by meaning, not just matching words.
Retrieval-Augmented Generation: giving the AI the right information at the right time
AI models can only look at so much information at once — you need to be selective.
Embeddings capture the meaning of text as numbers, so similar ideas have similar vectors.
RAG finds documents by meaning, not just keywords — then feeds the best matches to the AI.
The smarter the retrieval, the better the answer.