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Retrieval-Augmented Generation

Grounding language model output in retrieved enterprise content, with citations and permission-aware access.

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How a question reaches a governed answer

Knowledge, retrieval, model, reasoning, action — with the user’s permissions evaluated before retrieval, not applied to the answer afterwards.

What it is

Understanding Retrieval-Augmented Generation

RAG retrieves relevant content from a knowledge source and supplies it to a language model as context before it answers. This grounds the response in actual organizational content rather than in the model's training data, and allows answers to cite their sources.

Why it matters

It is the practical mechanism for applying language models to organizational knowledge without retraining. Citations make answers checkable, which is usually the difference between a system professionals will use and one they will not.

Problems it addresses

  • “Our AI assistant answers from general knowledge rather than from our documentation.”
  • “We cannot verify where an AI answer came from.”
  • “Our knowledge base is large, current and effectively unsearchable.”
  • “Retrieval would expose documents some users should not see.”

Capabilities

  • Ingestion and chunking

    Document parsing and segmentation that preserves meaning and metadata.

  • Embedding and indexing

    Vector representation with hybrid keyword and semantic retrieval.

  • Permission-aware retrieval

    Entitlement evaluated at query time, before content reaches the model.

  • Citation and provenance

    Every answer traceable to its source documents.

  • Freshness management

    Re-indexing and staleness detection as source content changes.

  • Retrieval evaluation

    Measuring whether the right content is being found, separately from answer quality.

Use cases

  • Enterprise knowledge assistance

    Question answering over policy, procedure and technical documentation.

  • Customer support augmentation

    Agent assistance grounded in current product and policy content.

  • Technical documentation search

    Semantic search over large specification sets.

Before you commit

Prerequisites, governance and risks

The questions that determine whether this will work in your environment, rather than in a demonstration.

What has to be true first

  • Content that is current — RAG faithfully surfaces outdated documents
  • Document-level access metadata
  • An evaluation set covering retrieval quality as well as answer quality

Governance

Permission-awareness must be enforced at retrieval, not by filtering results afterwards. The governing principle is that a user must never be able to obtain through AI what they could not access directly. Content lifecycle also becomes a governance concern: a RAG system is only as accurate as the documents behind it.

Risks

  • Retrieving outdated content and presenting it authoritatively
  • Permission bypass if entitlements are applied after retrieval rather than during it
  • Chunking that severs context and produces misleading fragments
  • Evaluating answer quality without separately evaluating retrieval quality

Where Retrieval-Augmented Generation leads

Every connection below is recorded in the content itself, and each one says why it is here.

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