What is grounding in AI? How real-world data improves AI accuracy and reliability

Grounding in AI is essential for developing systems that produce outputs aligned with the real world. While large language models generate responses based on patterns in training data, AI model grounding ensures these outputs are connected to real-world data in AI. By grounding large language models in current, verifiable information, we enhance their reliability, accuracy, and practical relevance.

What is grounding in AI and why does it matter for model accuracy?

In simple terms, grounding refers to connecting model outputs to real-world data, facts, or signals. Without grounding, even advanced models risk generating:

• Outdated information
• Generic or vague outputs
• Unverifiable claims

Grounding addresses these limitations by integrating retrieved, recent, or validated data into a model’s reasoning and generation process.

Why does grounding matter when building AI applications?

In financial analysis, decision-making depends on timely, specific, and accurate information. Ungrounded models can summarise general knowledge but often lack the precision needed for actionable insights.

For example, when analysing risks associated with new US import tariffs against China, a model without grounding might list general risks like supply chain disruptions or market uncertainty. While directionally correct, such outputs are too broad to guide real decisions.

How grounding in AI works in practice

At Bigdata.com, grounding is used in workflows that combine large reasoning models with real-time data retrieval to build dynamic mind maps. Here’s how the process works:

1. Planning and decomposition A large reasoning model breaks down a broad theme or risk scenario into structured components, creating a mind map with branches and sub-branches.
2. Formulating search queries The model generates targeted questions to retrieve supporting information from external data sources, such as news APIs.
3. Retrieving real-world data Using these queries, the system fetches recent content relevant to each sub-theme or risk.
4. Integrating data into outputs The model processes this data and refines the mind map, embedding insights anchored in current events.

Example: grounding risk mind maps

In a recent use case, Bigdata.com used grounding to analyse US import tariffs against China. The workflow was:

• A fast, low-cost model first created a basic mind map of potential risks, such as trade tensions and supply chain impacts.
• A large reasoning model then expanded this map, adding structured sub-scenarios across areas like regulatory risks and competitive risks.
• Finally, the model was grounded by retrieving and integrating news data from the Bigdata Search API.

This grounded mind map included:

• Political uncertainty – identifying recent policy shifts and their immediate effects
• Reputational risks – capturing narratives about consumer backlash against companies reliant on Chinese manufacturing
• Financial risks – reflecting investor concerns, projected cost increases, and margin pressures based on current reporting

Because these insights were tied to recent news, the outputs were not only broad and structured but also current, specific, and actionable.

Why grounding in AI is critical

Grounding supports:

• Relevance – keeps outputs aligned with evolving data
• Explainability – traces insights to real-world sources
• Trustworthiness – reduces hallucinations and enhances confidence in decisions

When you combine grounding with advanced reasoning models, AI workflows can produce outputs that remain up-to-date as new data emerges, powering applications such as thematic screening, risk analysis, and narrative tracking. Explore them here.

Key takeaway

Grounding in AI turns static model knowledge into living, data-anchored insights. As AI becomes central to analytical workflows, grounding remains fundamental to ensuring outputs are not just intelligent, but also reliable and decision-ready.