Grounding a large language model (LLM) means anchoring its outputs in reality, as defined by the organisation that is using it, to prevent hallucinations and mistakes. In domains where accuracy and trust are paramount, grounding can take the form of a machine-readable ontology.
Other AI grounding approaches include context graphs, neuro-symbolic AI and hybrid RAG, which focus on LLM training and access to quality data, as much as hard-and-fast rules.
Why is LLM grounding needed?
Because LLMs run on the basis of statistical probability, they can make things up (hallucinate) and get things wrong. This may be due to AI misunderstanding the intended meanings of words (semantic drift), for example, or using stale information to reason with.
LLMs are extremely fluent, and their capabilities have advanced swiftly thanks to billions of dollars of investment. However this comes at the price of precision and explainability; in other words, they lack grounding which regulated or compliance-heavy industries absolutely require before adopting new technology at scale.
Is grounding the obstacle to making AI profitable?
Some senior tech CEOs have already acknowledged the AI grounding issue. When asked about the possibility of the US economy being in an AI bubble at Davos in 2026, Palantir CEO Alex Karp redefined it as an ‘AI lag’. Technology is moving so fast, Karp argued, that enterprises are struggling to provide the structured and verified data that AI needs to deliver value.
Karp said: ‘If you just buy LLMs off the shelf and try to do any of these things that are regulated, it’s not precise enough. What you’re going to see, especially in America, is people trying to do something like Ontology by hand. Once you build a software layer to orchestrate and manage the LLMs in a language your enterprise understands, you actually can create value.’
Why does AI reliability matter?
Demand for accurate, relevant, and contextually appropriate responses to natural language (NLP) queries is spreading as the global economy digitalises. Yet lack of trust means that LLMs are mostly restricted to low value tasks in organisations.
This limits the productivity gains that digital transformation initiatives can achieve. The advent of orchestrated vertical AI agents has focused minds even more on LLM unreliability. Optimistic visions of an agentic web are likely to remain out of reach until this issue is addressed.
What’s the best way to ground AI?
In critical domains such as medical research, food industries, defence, and advanced manufacturing, knowledge structured as ontologies is well established. The need for interoperability to unite datasets and enable multidisciplinary collaboration, along with safety compliance, suggest that machine-readable ontologies are their only option if they want to deploy AI at scale.
For small and medium enterprises, however, the emergence of ontology-as-a-service platforms (OaaS) has made AI grounding more accessible. Some ontology softwares include tools that help with consensus-building around the definitions of controlled vocabularies, for example.
In domains where accuracy, traceability and interoperability are less mission-critical, knowledge graphs and RAG may fulfil requirements, especially when implemented by an experienced knowledge engineer.
What is the future of AI grounding technologies?
Developments in neuro-symbolic AI may throw up innovative solutions in the coming years. Combining slower but more rigorous procedural intelligence (symbolic) with the more intuitive, but ultimately less grounded approaches advanced by neural networks (LLMs), may yet deliver the best of both worlds.
A 2024 study by Hyunji Lee et al. introduced a new grounding metric to evaluate model capability. It suggests areas of improvement towards more reliable and controllable LLM applications.