Glossary
RAG. Retrieval Augmented Generation.
A technique for making AI answers accurate by retrieving relevant content from a knowledge base and feeding it to the language model before it generates a response. Instead of guessing from training data, the model writes its answer based on documents you actually have.
Without RAG, a language model answers from whatever it was trained on. Often months or years old, mostly scraped from the internet. With RAG, the model gets your actual documents in its context window first, then writes the answer from those. The difference between guessing and knowing.
What this looks like in practice
Why it matters now.
Foundation models hallucinate when asked about anything they weren't trained on. RAG is how production AI products give accurate answers about specific topics (your company, your customers, your industry) without retraining the model. Without it, you're stuck either accepting confident wrong answers or fine-tuning a model every time your data changes.
Where semantic similarity stops working.
"Find me anything similar to procurement playbooks" works great. "Find me the deal where Acme paid exactly $47,500" doesn't. Semantic search returns deals near that amount, with similar customer names, in adjacent quarters. Useless. The same retrieval that nails prose questions fails completely on structured data, exact-match lookups, and "show me ALL X" exhaustive queries. Most production AI products either ignore this gap or wedge in a band-aid.
Where multi-document answers break it.
"Who owns the contract renewal for the customer attached to last quarter's launch?" requires connecting a project to a customer to an owner. Vector RAG retrieves one document at a time. It can't follow the chain. That's a different problem and needs a different architecture.
RAG is one tool. Not the whole product.
We use RAG as one of three memory architectures inside Simply Asking. Vector retrieval for unstructured documents. Knowledge graph traversal for relationships. Tabular query for structured data. The right architecture for each question, automatically.
Bring your knowledge.Simply ask.
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