Quick Answer
Grounding means making the model work from supplied context or verifiable sources instead of free-associating from general training patterns. It does not eliminate errors, but it makes checking easier.
Use this guide when
The reader wants to lower hallucination risk in AI answers.
Working Method
The practical move is to make the model's job visible. Before you ask for the final output, define the important choices you do not want the model to guess.
- Provide the source material the answer should use.
- Tell the model to distinguish source-backed claims from assumptions.
- Ask for direct references to sections or excerpts when appropriate.
- Forbid invented citations, statistics, or quotes.
- Use a verification pass before relying on the output.
Prompt Example
Too vague
Explain what our policy says.More useful
Using only the policy excerpt below, answer the employee's question. If the excerpt does not contain enough information, say what is missing. Do not use outside knowledge, invent policy language, or provide legal advice.Common Pitfalls
- Asking for a sourced answer without providing sources.
- Letting the model fill missing policy details.
- Confusing a grounded summary with a verified final answer.
How to Judge the Answer
A better prompt is only useful if the answer becomes easier to evaluate. Before using the response, check whether it meets the standard you set.
- The answer points back to supplied material.
- Missing information is stated plainly.
- No invented quotes, links, or numbers appear.
FAQ
Does grounding stop hallucinations?
No. It reduces some risks and makes errors easier to catch, but important output still needs review.
What if the source material is incomplete?
Ask the model to say what is missing instead of guessing.
Sources
Selected references that informed this guide:
- AI Risk Management Framework NIST
- Overview of prompting strategies Google Cloud
- Prompt engineering overview Anthropic