Retrieval-Augmented Generation (RAG)

Retrieval-augmented generation is a technique that grounds a large language model in your own data. Before answering, the system retrieves relevant documents from a knowledge base and gives them to the model as context, so responses reflect your specific, current information instead of only the model's training data.

Retrieval-augmented generation (RAG) connects a large language model to a searchable store of your content — policies, product docs, past tickets, contracts. When a question comes in, the system first retrieves the most relevant passages, then asks the model to answer using that retrieved material as context.

RAG addresses two real limits of LLMs on their own: they do not know your private or recent information, and they can invent answers. By grounding responses in retrieved source text, RAG keeps answers tied to your data and makes them easier to cite and verify. Quality depends heavily on how the content is prepared, chunked, and searched.

How TwoApps applies this

We use RAG to ground assistants and agents in a client's own knowledge so answers stay accurate and on-policy.
We keep the source visible where it matters, so a person can check what an answer was based on.

Next Step

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