Grounding
Grounding is the process of anchoring AI responses in reliable enterprise data, enabling large language models to generate accurate, contextually relevant answers while preventing hallucinations.
Understanding grounding in AI systems
Grounding connects ai models to real-world information, helping large language models (LLMs) move beyond their general training to understand your specific business context. Instead of relying on broad knowledge, grounded AI systems tap directly into your organization's data — CRM records, documentation, chat logs, emails — to provide responses that reflect your actual environment.
Think of grounding as giving AI a direct line to your company's knowledge base. Without it, AI operates in a vacuum, drawing only from its training data. With grounding, it can reference your latest product specs, current policies, and real customer interactions.
How grounding prevents AI hallucinations
When LLMs operate without grounding, they often generate plausible-sounding but incorrect responses — what we call hallucinations. Grounding solves this through retrieval-augmented generation (RAG), which retrieves relevant information from your company's knowledge base before generating responses.
This approach ensures AI outputs are backed by actual data rather than fabricated details. Instead of guessing about your return policy or making up product features, grounded AI references your real documentation to provide accurate answers.
Grounding techniques and implementation
Modern enterprise systems use RAG to ground AI responses in real data. While basic implementations might simply copy relevant text into prompts, sophisticated platforms use secure vector databases to efficiently retrieve and process information. Notably, 73.34% of RAG implementations occur in large organizations, driven by the need for scalable, secure solutions.
Modern enterprise systems use RAG to ground AI responses in real data. While basic implementations might simply copy relevant text into prompts, sophisticated platforms use secure vector databases to efficiently retrieve and process information. The global RAG market is projected to grow at a 49.1% CAGR, reaching $11.0 billion by 2030.
Advanced grounding techniques combine structured data from databases with unstructured content from documents, providing comprehensive context for AI responses. The key is matching the right information to each query while maintaining security and permissions.
At Glean, we've found that effective grounding requires more than just retrieval — it needs understanding of relationships between people, content, and activity across your organization.
Enterprise applications of grounding
Customer service teams use grounded chatbots that reference specific case histories and product documentation to provide accurate support responses. Notably, LinkedIn's customer service team reduced median issue resolution time by 28.6% using RAG with knowledge graphs.
Customer service teams use grounded chatbots that reference specific case histories and product documentation to provide accurate support responses.
Sales teams generate proposals grounded in current pricing, product details, and customer history rather than outdated or generic information.
Grounding significantly improves AI accuracy, but it's not perfect. Retrieved information might be outdated or irrelevant, leading to suboptimal responses. Even with strong grounding, LLMs can occasionally misinterpret or contradict their source material. Notably, 71% of organizations view GenAI as a risk due to concerns over data security and hallucinations. Additionally, 38% of executives report making incorrect decisions based on hallucinated AI outputs.
Success depends heavily on having high-quality, well-organized enterprise data. Poor data quality leads to poor grounding, which leads to poor AI responses. Organizations need to invest in data hygiene and organization to maximize grounding effectiveness. Notably, 92% of early AI adopters report measurable ROI, with returns averaging $1.41 per dollar invested through cost savings and revenue growth.
Limitations and challenges of grounding
Grounding significantly improves AI accuracy, but it's not perfect. Retrieved information might be outdated or irrelevant, leading to suboptimal responses. Even with strong grounding, LLMs can occasionally misinterpret or contradict their source material.
Success depends heavily on having high-quality, well-organized enterprise data. Poor data quality leads to poor grounding, which leads to poor AI responses. Organizations need to invest in data hygiene and organization to maximize grounding effectiveness.
Glean's approach to grounding
Glean's grounding implementation reflects our belief that AI should work for everyone, not the other way around. Our RAG architecture combines enterprise search expertise with strict permission controls, ensuring responses are both accurate and secure.
We've built grounding that continuously learns from your organization's data and usage patterns, delivering increasingly relevant results while maintaining data privacy and compliance. By grounding AI in your company's knowledge graph — understanding relationships between people, content, and activity — we provide context that goes beyond simple keyword matching.
Our approach ensures that when you ask about "the Q3 budget," the AI knows which Q3, which budget, and whether you have permission to access that information.
Frequently asked questions about grounding
What's the difference between grounding and fine-tuning?Fine-tuning permanently modifies an AI model through training, while grounding provides real-time context without changing the underlying model. Grounding is more flexible and doesn't require retraining when your data changes.
How does grounding differ from traditional search? Grounding goes beyond keyword matching to understand context and relationships, helping AI systems generate coherent, contextually appropriate responses rather than just returning search results. For example, GPT-4 has a hallucination rate of only ~3% while PaLM 2 Chat has rates up to 27%. Neural Chat 7B demonstrates competitive performance at just 2.8%, suggesting smaller models can be highly accurate.
How does grounding differ from traditional search?
Grounding goes beyond keyword matching to understand context and relationships, helping AI systems generate coherent, contextually appropriate responses rather than just returning search results.
What types of data can be used for grounding?
Any enterprise data can serve as grounding material — structured database records, unstructured documents, emails, chat logs, wikis, and more. The key is ensuring the data is accessible and properly indexed.
How do you measure grounding effectiveness?
Success is measured through accuracy rates, hallucination reduction, and user feedback, with continuous monitoring to ensure responses remain reliable and relevant over time.
