Products
Quantum
Research
Resources
About
Company
All Resources Try DeepNova
Decision guide

Grounded retrieval vs RAG

Both augment LLM outputs with external information. They solve different problems. This guide covers definitions, mechanisms, and a decision framework.

Neutral comparison | Decision matrix | When each fits

What RAG does

Retrieval-Augmented Generation chunks documents, embeds them in a vector store, retrieves the closest matches by similarity, and injects them into the prompt. The LLM generates a response using the retrieved context.

RAG is simple, well-understood, and works well for internal knowledge bases where provenance tracking is optional. It reduces hallucination by providing relevant context, but does not eliminate it. Retrieved chunks can be ambiguous, and the LLM can still generate claims not directly supported by the retrieved material.

What grounded retrieval does

Grounded retrieval retrieves candidate evidence, checks relevance against the query, verifies provenance, and returns structured output with source links. Every claim in the output is traceable to a specific source. The output is auditable: you can check why the system said what it said.

Grounded retrieval is more complex than RAG. It requires a relevance verification step, provenance tracking, and structured output handling. The tradeoff is higher reliability and auditability.

Key differences

RAG

× Retrieves by vector similarity
× No built-in provenance tracking
× Output is natural language (unstructured)
× Hallucination still possible from ambiguous chunks

Grounded retrieval

✓ Retrieves by relevance with verification
✓ Every claim linked to source
✓ Output is structured JSON with evidence graphs
✓ Claims are auditable and traceable

Decision matrix

Use RAG when: internal documentation, speed is the priority, provenance is optional, and the cost of a wrong answer is low. RAG is the right choice for internal search, knowledge bases, and developer tooling where users can verify outputs themselves.

Use grounded retrieval when: customer-facing applications, compliance matters, citations are required, trust is the product, or you need to audit why the system produced a specific answer. Grounded retrieval is the right choice for legal, medical, financial, and any context where an unsupported claim creates liability.

Where DeepNova fits

DeepNova is a grounded retrieval engine that returns structured JSON with evidence graphs and source links. It is designed for teams building AI products where output reliability matters. Available as an MCP server and Developer API.

For teams that also need to reduce AI API costs, the NOVA Platform bundles DeepNova with NOVA Token Optimizer at 20% off.

Frequently asked questions

What is the difference between grounded retrieval and RAG?
RAG retrieves text chunks by vector similarity and injects them into the prompt. Grounded retrieval retrieves evidence with provenance, verifies relevance, and returns structured output with source links. Both augment LLM outputs but solve different problems.
When should I use RAG vs grounded retrieval?
Use RAG when working with internal documentation where speed matters and provenance is optional. Use grounded retrieval when building customer-facing applications where compliance matters, citations are required, or trust is the product.
Can RAG prevent AI hallucinations?
RAG reduces hallucination by providing relevant context, but does not eliminate it. Retrieved chunks can be ambiguous, and the LLM can still generate claims not supported by the retrieved material. Grounded retrieval addresses this by tying every claim to a verifiable source.