Grounding

Grounding constrains a generative AI model to verified sources of truth, dramatically reducing hallucination. The most common technique is retrieval-augmented generation (RAG), where the system retrieves relevant documents from a trusted knowledge base and includes them in the prompt before the model generates an answer. Other grounding approaches include database lookups ("according to our customer database..."), API calls (live stock prices, weather data), and citation requirements that force the model to point to sources. Modern AI products like Bing Copilot, Perplexity, Google's AI Overviews, and ChatGPT's web browsing rely heavily on grounding. Enterprise applications include legal research grounded in case databases, customer service grounded in product documentation, and medical decision support grounded in clinical guidelines. AI governance, AI compliance, and AI risk management programs increasingly require grounding for any enterprise AI system that produces factual claims, supporting responsible AI by making outputs verifiable and auditable.

Centralpoint Grounds AI in Your Own Trusted Content: Centralpoint by Oxcyon connects model output to enterprise sources you control — across OpenAI, Gemini, Llama, and embedded models. The platform meters every LLM call, keeps prompts and skills strictly on-prem, and embeds grounded chatbots into any portal with one JavaScript line.


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