Prove Your LLM Extractions: Google's Source Grounding Tool

You extract a product price from a 50-page PDF using an LLM. Your stakeholder asks where that number came from. You have two options: re-run the prompt and hope for consistency, or point to the exact paragraph that contains it.

Most developers are stuck with option one. Google's source grounding project aims to make option two standard practice.

The verification problem every LLM developer faces

When you ask an LLM to turn unstructured text into structured data—extracting dates from contracts, pulling specs from documentation, summarizing research papers—you're trading certainty for speed. The model returns clean JSON, but the chain of reasoning disappears. Did it hallucinate that revenue figure? Did it conflate two different product versions? Without attribution, debugging becomes archaeology.

Solo developers building document processors face this. Enterprise teams building compliance tools face this. Anyone who needs to defend an LLM's output to a human audience faces this. The question "where did this come from?" has no good answer when your extraction pipeline is a black box.

What source grounding actually means

Source grounding creates an audit trail. For each claim in your extracted data, the system maintains a pointer back to the text span that supports it. Extract a date? Here's the sentence it came from. Pull a number? Here's the paragraph. Generate a summary? Here's every source sentence that contributed.

This turns verification from guesswork into inspection. Instead of re-running prompts to check consistency, you examine the source mapping. Instead of trusting the model's confidence scores, you read the original text yourself. The extracted data becomes provable rather than plausible.

It also changes how you debug hallucinations. When an LLM invents information, source grounding surfaces it immediately—the claim has no corresponding text span. When it misinterprets context, you can see which passage led it astray.

How Google's approach works

The project tackles this through two mechanisms: attribution tracking during extraction and visualization afterward.

During extraction, the system instruments the LLM to maintain links between output claims and input text. Rather than just returning structured data, it returns structured data plus grounding annotations—coordinates pointing back to source documents. This requires the model to cite its work as it generates results.

The visualization layer makes those annotations useful. Developers see their extracted data alongside the original text, with visual connections showing which claims map to which passages. Click on an extracted field, and the source text highlights. Hover over a paragraph, and the derived claims appear. The tool transforms invisible attribution into a spatial interface you can navigate.

This works across different LLM providers, not just Gemini. The grounding mechanism sits at the application layer, making it useful for anyone building extraction pipelines regardless of their model choice.

Why 33K developers starred this project

The momentum suggests this hit a nerve. Source grounding solves a problem that transcends models or use cases. Whether you're processing legal documents, extracting metadata from research papers, or building a knowledge base from customer support tickets, you need attribution.

The timing matters. As LLM applications move from demos to production, the infrastructure problems become critical. Stakeholders want explanations. Auditors want verification. Users want trust. A tool that provides proof without requiring you to re-architect your pipeline has value.

Google released this as an open contribution rather than a proprietary Gemini feature. It signals that source grounding is infrastructure, not competitive advantage—a problem the entire field benefits from solving together.

Where this fits

This is one approach to a problem many teams are tackling. Retrieval-augmented generation systems handle similar challenges differently, embedding citations into generation rather than extraction. Some teams build custom logging layers that track token-level attribution. Others rely on deterministic extraction rules that guarantee traceability by avoiding generative models entirely.

Google's tool sits in a niche: teams that want to use LLMs for extraction but need the verification properties of traditional parsing. Solo developers get debugging tools without building custom infrastructure. Enterprise teams get audit trails for compliance requirements. Both get a way to answer "where did this come from?" with evidence instead of confidence scores.

The project is still under development—APIs and integration patterns are evolving. But the core insight holds: if LLM outputs are going to be trusted, they need to be traceable. Source grounding makes that traceability visible.