TL;DR

RAG (Retrieval-Augmented Generation) combines information retrieval with LLM generation. You retrieve relevant documents from a knowledge base, then use them as context for the LLM to generate answers. RAG reduces hallucinations by grounding outputs in source documents, but requires testing retrieval accuracy, context relevance, and generation quality. Choose local RAG for data privacy, cloud RAG for scalability.

RAG Systems — Architecture, Risks, and Testing

RAG systems combine retrieval and generation to produce accurate, grounded outputs. This guide covers RAG architecture, evaluation methods, and testing approaches.

What Is Retrieval-Augmented Generation (RAG)?

RAG combines information retrieval with LLM generation. You retrieve relevant documents from a knowledge base, then use them as context for the LLM to generate answers. This grounds outputs in source documents, reducing hallucinations.

RAG workflow:

  • User query comes in
  • Retrieval system searches knowledge base for relevant documents
  • Retrieved documents are passed as context to the LLM
  • LLM generates answer using retrieved context
  • Answer is returned with source citations

Read more: What Is Retrieval-Augmented Generation (RAG)?

Local RAG vs Cloud RAG — Which Should You Choose?

Choose local RAG when you need data privacy, compliance, or want to avoid cloud costs. Choose cloud RAG for scalability, managed infrastructure, and easier deployment.

Local RAG pros:

  • Data stays on-premises (privacy, compliance)
  • No cloud API costs
  • Full control over infrastructure
  • Works offline

Cloud RAG pros:

  • Scalable infrastructure (handles traffic spikes)
  • Managed services (less maintenance)
  • Access to latest models
  • Faster deployment

Read more: Local RAG vs Cloud RAG — Pros and Cons

How Do You Evaluate RAG Systems?

Evaluate RAG systems by testing retrieval accuracy, context relevance, and generation quality. Use metrics like:

  • Retrieval precision (relevant documents retrieved)
  • Context relevance (retrieved docs match query)
  • Answer accuracy (generated answer is correct)
  • Citation quality (sources are correctly cited)
  • Hallucination rate (answers not in retrieved context)

Read more: How to Evaluate RAG Systems

How Does RAG Reduce Hallucinations?

RAG reduces hallucinations by grounding outputs in retrieved source documents. The LLM generates answers based on provided context, not just its training data. However, RAG can still fail if:

  • Retrieval returns irrelevant documents
  • LLM ignores retrieved context
  • Knowledge base is incomplete or outdated
  • Query doesn't match any documents

Read more: How RAG Reduces Hallucinations (And When It Fails)

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Frequently Asked Questions

Does RAG completely eliminate hallucinations?

No. RAG reduces hallucinations by grounding outputs in source documents, but hallucinations can still occur if retrieval fails, context is ignored, or the knowledge base is incomplete. Always test and monitor hallucination rates.

How do I choose between local and cloud RAG?

Choose local RAG for data privacy, compliance requirements, or cost control. Choose cloud RAG for scalability, managed infrastructure, and faster deployment. Many organizations use hybrid approaches.

What's the best vector database for RAG?

Popular choices include Pinecone (cloud), Weaviate (open-source), Chroma (lightweight), and Qdrant (self-hosted). Choose based on scale, latency requirements, and deployment preferences.

How often should I update my RAG knowledge base?

Update when source documents change, when users report outdated information, or on a regular schedule (weekly/monthly). Set up automated pipelines to sync knowledge bases with source systems.

Can RAG work with structured data?

Yes. RAG works with structured data (databases, APIs) by converting structured records to text, or using hybrid retrieval that combines vector search with structured queries. This is useful for enterprise data.