Grounding Methods
How to ground AI responses
Every feature designed to help your team work smarter with AI.
RAG grounding
Retrieve relevant documents and include them in the prompt context so the model's responses are based on verified information.
Context provision
Include specific facts, data, and reference material in the prompt so the model has accurate information to work from.
Citation requirements
Instruct the model to cite sources and reference specific parts of the provided context in its responses.
Uncertainty acknowledgment
Prompt the model to explicitly state when it is unsure or when the provided context does not contain the answer.
Verification steps
Build fact-checking and verification into your AI workflow, either within the prompt chain or as a human review step.
Template engineering
Create grounded prompt templates that consistently include context, citation instructions, and uncertainty handling.
Benefits
Why grounding matters for AI teams
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.
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