Scattered tips reduce hallucinations, but a framework lets you reduce them reliably. A framework gives you a shared vocabulary, a default order of operations, and a way to know what you have and have not addressed. This article introduces one—the GROUND model—for designing prompts that resist fabrication.
GROUND stands for five stages: Gather the source, Restrict to it, Offer an exit, Underwrite with evidence, and Nail down with verification. The name is a mnemonic, not a magic formula; the value is in the discipline of moving through the stages in order and knowing what each one buys you. Like any model, it is a default to apply with judgment, not a script to follow blindly.
For the foundational concepts the framework organizes, see Stop Your Model From Inventing Facts at the Prompt Layer.
G: Gather the Source
The first stage is securing the material the answer should come from. Everything downstream depends on it.
What this stage does
It replaces the model's unreliable memory with authoritative facts. A prompt with no gathered source is a prompt destined to guess, no matter how careful the wording.
When it matters most
Whenever the answer hinges on specific facts—numbers, names, policies, product details. For purely creative or opinion tasks where there is no ground truth, this stage may not apply, and you lean on the later stages instead.
R: Restrict to It
Gathering the source is not enough if the model is still free to blend in memory. The second stage closes the book.
What this stage does
It instructs the model to answer only from the gathered source and to ignore its own prior knowledge. The restriction is what turns supplied context into a binding constraint rather than a suggestion.
When it matters most
On any grounded task. Skipping it is one of the most common reasons a prompt with good context still fabricates—the model treats the context as optional. This failure is detailed in 7 Prompting Habits That Make AI Fabricate More, Not Less.
O: Offer an Exit
The third stage gives the model permission to admit it cannot answer, which it will not do by default.
What this stage does
It defines, concretely, what the model should say when the source does not contain the answer. This converts a forced guess into an honest abstention and addresses the model's tendency to answer everything.
When it matters most
Always, but especially when the source is incomplete or when users ask questions outside its scope. The risk here is over-correction—too strong an exit makes the model refuse answerable questions—so the stage requires calibration, as discussed in Build a Fabrication-Resistant Prompt in Eight Moves.
U: Underwrite With Evidence
The fourth stage requires the model to back each claim with the source passage that supports it.
What this stage does
It forces a self-check before the model commits and makes unsupported claims visible as gaps. An answer that cannot point to its source becomes a candidate for abstention rather than a fabrication.
When it matters most
On factual and high-stakes tasks where a wrong claim carries cost. The caveat: models can cite real passages that do not actually support the claim, so evidence reduces fabrication but does not eliminate the need for the final stage.
N/D: Nail Down With Verification
The final stage adds an independent check that confirms the answer holds up against the source.
What this stage does
A separate pass, framed independently, evaluates whether the answer is genuinely supported. Because the generation step tends to rationalize its own output, an independent check catches errors the earlier stages approved.
When it matters most
On the highest-stakes output, where a confident wrong answer causes real harm. It roughly doubles cost and latency, so for cheap-error tasks you may stop at the earlier stages. To see the full model applied end to end, read Grounding Prompts in Action: Five Scenarios That Tell.
Applying GROUND in Practice
The stages are a default order, not a rigid gate. Knowing how to flex them is what makes the framework usable.
Scaling by stakes
Low-stakes tasks may use only G, R, and O. High-stakes tasks run all five. Let the cost of an error determine how far down the model you go.
Diagnosing failures by stage
When a prompt fabricates, walk the stages to find the missing one. No source? Gather failed. Context present but ignored? Restrict failed. Confident on unanswerable questions? Exit failed. The framework doubles as a diagnostic checklist.
Avoiding the over-correction trap
The Offer-an-exit stage can swing too far into refusal. Always pair it with measurement of unnecessary abstention so the model stays calibrated rather than merely cautious.
Why a Named Model Beats Loose Tips
It is fair to ask whether wrapping known techniques in a mnemonic adds anything. In practice, the naming changes how teams work in three concrete ways.
Shared vocabulary speeds review
When a teammate says a prompt skipped the Restrict stage, everyone knows exactly what is missing without re-explaining the concept. A shared name turns a vague worry—"this prompt feels like it might fabricate"—into a precise, actionable diagnosis. Reviews get faster and more consistent because the team is pointing at named stages rather than describing symptoms.
A default order prevents wasted effort
The framework encodes the dependency between stages, so people do not waste time adding verification to a prompt that was never grounded, or writing evidence requirements with no source to cite. The order is the accumulated lesson that certain safeguards are meaningless without their prerequisites. Following it keeps effort from landing in the wrong place.
Coverage becomes checkable
With named stages, "have we addressed fabrication here?" becomes a concrete audit: which of the five stages are present, and which are deliberately skipped for stakes reasons? Loose tips offer no such accounting, so teams can never be sure what they have covered. The framework converts a fuzzy sense of diligence into a list you can actually verify against.
Frequently Asked Questions
Do I always run all five stages?
No. The stages scale with stakes. Gather, Restrict, and Offer-an-exit form the core that applies to almost any grounded task. Underwrite-with-evidence and the verification pass add cost and matter most when a wrong answer is expensive. Match the depth to the risk.
How is GROUND different from just listing tips?
The value is the order and the diagnostic structure. The stages build on each other—restricting is meaningless without gathering, verifying is wasteful without grounding—and when a prompt fails, walking the stages tells you which one is missing. A flat list of tips offers neither sequence nor diagnosis.
What if my task has no source to gather?
Then the Gather and Restrict stages do not apply, and you lean harder on Offer-an-exit to prevent guessing, while accepting higher fabrication risk. Reserve sourceless tasks for low-stakes use, because without grounding the framework's strongest protections are unavailable.
Can the verification stage be skipped to save cost?
Yes, for low-stakes output where errors are cheap to absorb. The verification pass roughly doubles cost and latency, so it earns its place only when a confident wrong answer carries real risk. The earlier stages still do most of the work.
How does the framework handle over-correction?
The Offer-an-exit stage explicitly carries the over-correction risk, and the framework pairs it with measuring unnecessary abstention. The target is calibration—answering when the source supports it, abstaining when it does not—rather than maximizing caution, which would make the model refuse answerable questions.
Key Takeaways
- GROUND organizes hallucination reduction into five stages: Gather the source, Restrict to it, Offer an exit, Underwrite with evidence, and Nail down with verification.
- Gather, Restrict, and Offer-an-exit form the core that applies to nearly any grounded task; the later stages scale with stakes.
- The framework doubles as a diagnostic—walk the stages to find which one a fabricating prompt is missing.
- The Offer-an-exit stage carries an over-correction risk and must be paired with measuring unnecessary abstention.
- Underwrite-with-evidence and verification reduce subtle errors but add cost, so reserve them for high-stakes output.