What problem does RAG solve?

It lets a model answer from your specific, up-to-date data instead of only its training, which reduces hallucination and stale answers.

Why are my RAG answers still wrong?

Usually retrieval, not the model — poor chunking or ranking feeds the wrong context. Fix retrieval and reranking first.

Does the prompt matter in RAG?

Yes — how you instruct the model to use (and not exceed) the retrieved context is decisive. Oort has grounding-focused prompts to start from.

Retrieval-Augmented Generation

RAG news — vector databases, retrieval techniques, and grounding LLMs in your own data without hallucinating.

What to know

Retrieval-Augmented Generation grounds an LLM in your own data by retrieving relevant context at query time — the standard way to cut hallucination and answer from private knowledge. The moving parts are chunking, embeddings, retrieval, and reranking. This hub tracks techniques and tooling.

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Retrieval-Augmented Generation — FAQ

What problem does RAG solve?: It lets a model answer from your specific, up-to-date data instead of only its training, which reduces hallucination and stale answers.
Why are my RAG answers still wrong?: Usually retrieval, not the model — poor chunking or ranking feeds the wrong context. Fix retrieval and reranking first.
Does the prompt matter in RAG?: Yes — how you instruct the model to use (and not exceed) the retrieved context is decisive. Oort has grounding-focused prompts to start from.