# Source grounding

Source grounding is the practice of tying an AI model’s output to specific, verifiable external sources retrieved when a question is asked, so its claims trace back to real documents instead of training data alone.

## What is source grounding?

Google explains the idea in its [Google Cloud grounding overview](https://docs.cloud.google.com/gemini-enterprise-agent-platform/models/grounding/overview). It states: “In generative AI, grounding is the ability to connect model output to verifiable sources of information.” A grounded system retrieves relevant documents when a user asks a question. It then writes an answer from that content and usually adds citations.

A language model learns from a fixed training set with a cutoff date. Source grounding adds fresh, external context at answer time. The model can then respond with information it never saw during training.

Source grounding matters because ungrounded models can invent facts, and grounding ties each answer to real, retrievable evidence instead.

Grounding gives AI agents current, cited access to the web instead of training data alone. That makes their answers easier to trust and easier to check.

## Key characteristics

Meta AI researchers ran a foundational study on retrieval. In the [Meta AI retrieval study](https://arxiv.org/abs/2104.07567), human evaluations found that retrieval augmentation “substantially reduce[s] the well-known problem of knowledge hallucination in state-of-the-art chatbots.” Grounded systems share a few defining traits.

- **Retrieval at inference time:** the system fetches relevant passages when a user asks, not from frozen training data.
- **Answers conditioned on sources:** the model writes only from the passages it retrieves.
- **Traceable citations:** grounded answers carry source URLs and excerpts, so you can audit each claim.
- **Distinct from RAG:** retrieval-augmented generation (RAG) is one architecture for grounding, while grounding is the outcome.

## Example

Suppose an AI agent needs to answer a factual question about a recent world event. It calls [Parallel’s Search API](https://parallel.ai/products/search), which returns ranked URLs and dense excerpts from the live web. The agent then writes its answer and cites each page, so you can trace every claim.

![Parallel’s web search quality benchmarks showing accuracy across search engines](https://cdn.sanity.io/images/5hzduz3y/production/50aa4294bd61a539e76907fcb537c7db5266ebd7-1920x1080.png)

_Parallel’s web search quality benchmarks measure accuracy across search engines. Source: __[Parallel benchmarks](https://parallel.ai/benchmarks)__._

Retrieval quality shapes grounding quality. Parallel maintains its own web index of billions of pages and adds millions more each day, which keeps grounded answers current. On public benchmarks like SimpleQA and FRAMES, Parallel reports the highest accuracy at the lowest cost per query. Its Search API returns those results in 200 milliseconds to three seconds.

## Related terms

- [web search API](https://parallel.ai/articles/what-is-a-web-search-api): the tool an agent calls to fetch grounding sources.
- [semantic search](https://parallel.ai/articles/what-is-semantic-search): the retrieval method that matches meaning rather than keywords.
- [AI agents](https://parallel.ai/articles/what-is-an-ai-agent): systems that use grounded context to act and answer.
- [deep research](https://parallel.ai/articles/what-is-deep-research): tasks across many sources that depend on grounded, cited retrieval.

## FAQ

**What’s the difference between source grounding and RAG?**

Retrieval-augmented generation (RAG) is one architecture that retrieves documents and feeds them to the model. Source grounding is the broader goal of anchoring outputs to verifiable sources, and RAG is the most common way to reach it.

**Is source grounding the same as a citation?**

No, a citation is the reference that grounding produces, while grounding is the process of constraining generation to retrieved sources.

**Does source grounding stop AI hallucinations?**

It reduces them and makes the rest traceable. It can’t guarantee correctness, though, because a model can ground a claim in a source that’s outdated or wrong.

Parallel gives you grounded, cited web results built for AI agents. [Start Building](https://docs.parallel.ai/home).