DefinitionAccuracyReliability

What is grounding in AI?

Grounding in AI is the practice of anchoring model outputs to verified, factual information sources. It connects AI-generated text to real data, documents, or knowledge bases, reducing hallucinations and improving the reliability of AI responses.

Grounding Methods

How to ground AI responses

Every feature designed to help your team work smarter with AI.

01

RAG grounding

Retrieve relevant documents and include them in the prompt context so the model's responses are based on verified information.

02

Context provision

Include specific facts, data, and reference material in the prompt so the model has accurate information to work from.

03

Citation requirements

Instruct the model to cite sources and reference specific parts of the provided context in its responses.

04

Uncertainty acknowledgment

Prompt the model to explicitly state when it is unsure or when the provided context does not contain the answer.

05

Verification steps

Build fact-checking and verification into your AI workflow, either within the prompt chain or as a human review step.

06

Template engineering

Create grounded prompt templates that consistently include context, citation instructions, and uncertainty handling.

Benefits

Why grounding matters for AI teams

Dramatically reduce AI hallucinations by anchoring outputs to verified data
Improve trust in AI-generated content across your organization
Enable AI usage for knowledge-critical tasks that require accuracy
Standardize grounding practices across your team through shared templates
Support compliance requirements for AI output accuracy and traceability
Build reliable AI workflows that stakeholders can trust for decision support

FAQ

Frequently asked questions

What is the difference between grounding and RAG?

RAG is a specific technique for grounding that uses document retrieval. Grounding is the broader concept of connecting AI outputs to verified information, which can include RAG, context provision, citation requirements, and verification workflows.

How does TeamPrompt help with grounding?

TeamPrompt helps teams share grounded prompt templates that include context provision, citation instructions, and verification steps. Consistent grounding practices are easier to maintain with standardized templates.

Does grounding guarantee accuracy?

Grounding significantly improves accuracy but does not guarantee it. Models may still misinterpret context or generate unsupported claims. Verification workflows provide an additional safety layer.

How it works

Three steps from install to full AI security coverage.

1

Install

Add the browser extension to Chrome, Edge, or Firefox — or use the built-in AI chat. No proxy or VPN needed.

2

Configure

Enable the compliance packs for your industry, set DLP rules, and add your team's prompts to the shared library.

3

Protected

Every AI interaction is scanned in real time. Sensitive data is blocked before it leaves the browser. Your team has a full audit trail.

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