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AI Glossary

Knowledge Grounding

Using approved internal context to improve response relevance in AI workflows.

TL;DR

  • —Using approved internal context to improve response relevance in AI workflows.
  • —Knowledge Grounding shapes how organizations design controls, ownership, and operating discipline around AI.
  • —Use the related terms and explanation below to connect the definition to real enterprise rollout decisions.

In Depth

Knowledge Grounding is the process of tethering a generative AI model's responses to specific, verified sources of truth, rather than relying on the model's generalized pre-training data. Without grounding, LLMs are prone to 'hallucinations'—confidently generating false or entirely fabricated information. For an enterprise relying on AI to draft legal contracts, assist customer support, or write code, hallucinations are a critical operational risk.

Grounding typically works by intercepting the user's prompt, automatically retrieving highly relevant internal documents (like HR policies, product manuals, or past support tickets), and appending those documents to the prompt before sending it to the LLM. The AI is then instructed to answer the user's question explicitly and exclusively based on the provided context. If the answer isn't in the provided documents, a properly grounded AI will respond with 'I don't know' rather than guessing.

In enterprise governance, the concept of Intentional Knowledge Grounding is crucial. A common mistake is attempting to ground an AI by simply giving it access to every file in a corporate SharePoint or Google Drive. This inevitably leads to the AI resurfacing forgotten, highly sensitive documents (like executive compensation plans) to unauthorized users due to legacy permission errors. Intentional grounding, as utilized by Remova's Team Workspaces, requires department leads to actively curate specific, sanitized datasets for the AI to use, ensuring both high accuracy and strict data security.

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Related Terms

Retrieval-Augmented Generation (RAG)

A method where AI responses are informed by retrieved reference content.

Usage Analytics

Operational reporting on AI adoption, policy events, and spending trends.

Policy Guardrails

Control checks that evaluate AI inputs and outputs against organization policy.

AI Governance

The policies, controls, and operating practices used to manage AI usage safely at scale.

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Knowledge Hub

Glossary FAQs

Fine-tuning involves permanently altering the underlying weights of an AI model by training it on thousands of examples, which is expensive and slow to update. Grounding (often done via <a href='/glossary/rag'><a href='/glossary/rag'>RAG</a></a>) simply provides the AI with reference documents at the moment a question is asked, making it much faster, cheaper, and easier to update as knowledge changes.
Standard LLMs guess the next most likely word based on the entire internet. A grounded LLM is given a strict system prompt (e.g., 'Only answer based on the following text: [Inserted Document]'). This artificially limits the model's 'creativity' and forces it to act as a summarization and extraction engine rather than a creative writer.
Corporate drives are notoriously messy. They contain draft documents, obsolete policies, and files with overly broad permissions. If an AI indexes the entire drive, it may provide an employee with an outdated vacation policy from 2018, or worse, summarize a confidential spreadsheet that was accidentally shared with 'Everyone in the company'.

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