Retrieval Augmented Generation (RAG)

Large language models know a lot, but they do not know your company's internal data. They also make things up when they are unsure. RAG solves both problems by adding a retrieval step before generation.

Here is how it works. A user asks a question. The system converts that question into a vector embedding and searches a vector database for the most relevant documents. Those documents get passed to the LLM as context, along with the original question. The LLM then generates an answer based on the retrieved content instead of relying only on its training data.

The result is answers that are grounded in your actual documents, policies, product specs, or whatever you have indexed. This matters for enterprise use cases where accuracy is non-negotiable. A support agent pulling from your knowledge base needs to cite real policies, not generate plausible-sounding ones.

RAG architectures vary in complexity. A basic setup has an embedding model, a vector database, and an LLM. More advanced systems add re-ranking (scoring retrieved documents for relevance), chunking strategies (splitting documents into the right-sized pieces), and hybrid search (combining keyword and semantic search).

Compared to fine-tuning, RAG is faster to set up and easier to update. When your data changes, you re-index the documents. No model retraining needed.