Retrieval-Augmented Generation (RAG) is a technique that makes an AI model answer based on specific, retrieved documents rather than only on what it learned during training. Instead of generating from memory, the system first retrieves the relevant source material, then generates its answer grounded in that material.
RAG matters because it addresses the core weakness of language models used alone: they can produce fluent but unsupported statements. By forcing the model to work from retrieved, real documents, RAG ties its output to a verifiable source, essential anywhere accuracy and traceability are non-negotiable, like finance.
This grounding principle is central to why Phacet is trustworthy. Its agents do not invent figures from general knowledge; they work from the company's own documents and check against them. The agent that extracts data from your contracts and leases pulls terms from the actual document, the agent that verifies invoices against contract terms compares each line to the real agreement, and the agent that checks documentary file completeness confirms the supporting documents are there. Every output traces back to a source through a native audit trail.
RAG grounds AI answers in retrieved, real documents instead of memory. For Phacet, that grounding is the difference between an agent that asserts and one that verifies against the source, every time.
