Research · Generative & Agentic AI

RAG & Knowledge Systems

Retrieval-augmented generation (RAG) connects AI models to your own documents and data so answers are grounded in approved sources. For most enterprise knowledge use cases, the quality of retrieval matters more than the choice of model.

How it works

A RAG system first searches a knowledge source for content relevant to a question, then passes that content to the model along with the question. The model answers based on the retrieved material and can cite its sources. Knowledge sources are often indexed in vector databases, sometimes combined with keyword search and knowledge graphs.

What makes it work well

• Curated, current and authoritative content, with outdated material removed.
• Sensible chunking and metadata so the right passages are found.
• Retrieval that respects each user’s access permissions.
• Citations so users can check answers against sources.
• Evaluation of both retrieval accuracy and answer quality.

Why it matters

RAG reduces, but does not eliminate, incorrect answers by giving the model relevant facts. It also lets organizations keep knowledge current without retraining models. Its weaknesses mirror the knowledge base: if content is outdated, contradictory or overshared, the answers will be too.

Risks and pitfalls

• Indexing everything, including outdated and sensitive content.
• Ignoring document-level permissions during retrieval.
• Prompt injection through documents placed in the knowledge base.
• Judging the system on demos instead of systematic evaluation.

How to get started

• Choose one well-bounded knowledge domain with an accountable content owner.
• Clean and tag the content before indexing it.
• Enforce source permissions in retrieval.
• Build a test set of real questions with expected answers.