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You cannot guarantee that an AI model will never invent a fact, citation, quote, or calculation. You can, however, make made-up answers less likely and easier to detect by narrowing the task, supplying evidence, defining how uncertainty must be reported, breaking complex work into checked steps, and reviewing the result against original sources.
The most dependable approach is a prompt that acts like a small specification: it tells the model exactly what to do, what information it may use, what to do when evidence is missing, and how to expose its assumptions. The six tactics below work together, with the strongest workflow reserved for decisions where an error could cause real harm.
What are AI hallucinations, and can prompting stop them?
An AI hallucination is a plausible-sounding output that is unsupported, false, or presented with more certainty than the available evidence allows. Common examples include an invented study, a quotation that no source contains, a citation with a real-looking title but a nonexistent paper, or a correct-looking calculation based on an unstated assumption.
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These errors are a consequence of how generative models produce language. As the OpenAI Help Center puts it, “ChatGPT is designed to provide useful responses based on patterns in data it was trained on.” That design does not give the model a built-in guarantee that every sentence is true, current, or traceable to a source. OpenAI also warns that outputs can include fabricated quotes, studies, citations, and references.
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Prompting changes the conditions under which an answer is produced; it does not turn a model into an authority. A narrow task, an evidence pack, an explicit uncertainty rule, and a separate verification pass provide stronger controls than simply asking the model to “be accurate.” Important claims still need independent checking against primary or trusted sources.
Which hallucination-control tactic should I use first?
Start with a clear task and output contract, then add evidence and an uncertainty policy. The amount of checking should rise with the consequences of an error.
| Tactic | Control it adds | Relative effort | Best fit |
|---|---|---|---|
| Define the task and output contract | Constrains scope, audience, format, and acceptance criteria | Low | Every request |
| Ground the answer in supplied evidence | Limits factual claims to identifiable material | Medium | Research, summaries, policy and technical work |
| Set uncertainty and citation rules | Prevents unsupported certainty and distinguishes fact from inference | Medium | Any answer where traceability matters |
| Decompose complex work | Exposes assumptions, intermediate checks, and unresolved questions | Medium | Calculations, planning, multi-part analysis |
| Require a verification pass | Checks each material claim against its source | High | Consequential or publishable outputs |
| Iterate with concrete error feedback | Makes corrections traceable instead of repeating vague instructions | Low to medium | Any recurring workflow |
A short clarity prompt is fast. A context-rich prompt with citations and verification takes longer but gives you a stronger audit trail. For high-stakes uses, the extra effort is justified.
How do I define the task and output contract?
Describe the exact job before asking for prose or an answer. State the audience, scope, format, length, and acceptance criteria, and ask the model to list missing information before it starts.
Specify the job in one sentence
Use a verb and a concrete deliverable: “Extract the obligations in this contract into a table,” “Explain this error message to a new administrator,” or “Calculate the total using the supplied rates.” Avoid a broad request such as “Tell me everything about this.” A narrow objective gives the model fewer opportunities to fill gaps with assumptions.
Define the audience and boundaries
- Name the reader’s expertise and location when those affect the answer.
- State the time period, product edition, jurisdiction, or source set that applies.
- Say what is out of scope, such as recommendations, speculation, or information not present in the supplied material.
Make the output contract testable
Specify headings, table columns, required fields, maximum length, units, and what a missing value should look like. Add acceptance criteria such as “separate facts from inferences,” “show each calculation,” or “include a source for every material claim.” Ask for a preliminary list of assumptions and missing information; you can correct those before the draft is written.
Example
You are preparing a two-paragraph incident summary for an operations manager. Use only the incident log below. Include the time, affected service, observed symptom, confirmed cause, and next action. If a field is absent, write “not established in the log.” List any assumptions before drafting, and do not recommend fixes that are not recorded in the log.This contract does not make the model knowledgeable about the incident. It makes the allowed task and the acceptable shape of the answer explicit.
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How can supplied evidence ground an AI answer?
Give the model the source text, data, or an evidence pack and instruct it to use only that material for factual claims. Grounding is more reliable than asking for a general answer because every assertion has a defined place to come from.
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Build a usable evidence pack
Paste the relevant passages, rows, or excerpts rather than a vague description of a document. Label each item with an identifier, title, date, and page or section when available. Separate source text from your instructions with clear delimiters so the model can tell evidence apart from the task.
Require quote-level support
For claims that matter, ask the model to attach the source identifier and a short direct quotation or data reference. Anthropic recommends direct-quote grounding, while the U.S. Department of Energy describes grounding as using trusted external information. A quote lets a reviewer inspect whether the model stretched or misunderstood the source.
Tell it what to do with missing support
Instruct the model to mark a claim as UNSUPPORTED when no supplied item backs it. Do not ask it to “fill in the obvious parts.” If the source pack is incomplete, the correct output may be a list of unanswered questions rather than a complete-sounding narrative.
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Watch for evidence collisions
When two supplied sources disagree, require the model to show both positions, identify their dates or scope, and ask for a decision rule before choosing one. Silent reconciliation hides a potentially important conflict.
What uncertainty and citation policy should a prompt include?
Give the model explicit permission to say “I don’t know,” and require a source or an uncertainty label for every material claim. Also tell it to distinguish a directly supported fact from a calculation or an inference.
Use a small, consistent vocabulary
- FACT: directly stated or directly calculated from the supplied evidence.
- INFERENCE: a conclusion that follows from the evidence but is not stated verbatim.
- ASSUMPTION: an input you supplied or the model needs in order to proceed.
- UNSUPPORTED: a claim for which no supplied evidence is available.
You can replace these labels with terms used by your team, but keep them stable across requests. Consistent labels make review faster and prevent a confident tone from hiding an evidence gap.
Define the citation boundary
Say which claims need citations and what counts as a citation: a source identifier, page number, quoted passage, dataset row, or calculation line. A citation format is not proof that a source exists. If the model cannot locate the claim in the supplied material, it must retract the claim or label it unsupported.
Allow calibrated language
Require wording such as “the source states,” “the evidence suggests,” or “not established” where appropriate. Do not force a yes-or-no answer when the evidence supports only a range or a conditional conclusion.
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How do I decompose complex work into safer steps?
Split research, calculations, or planning into stages and ask for assumptions, intermediate checks, and unresolved questions before the final answer. Decomposition turns one opaque generation into several inspectable decisions.
A practical four-stage sequence
- Inventory the inputs. Ask the model to list the relevant facts, variables, source items, and missing fields without drawing a conclusion.
- State assumptions and method. Require the formula, interpretation rule, or decision criteria it will use. You can correct an assumption before it contaminates later steps.
- Work through intermediate results. Request subtotals, extracted clauses, or a claim-to-source mapping. Keep units and identifiers visible.
- Produce the final output. Only after the previous stages are accepted should the model write the summary, recommendation, or plan.
Use delimiters and explicit parameters
Place source material between markers such as <source> and </source>, and put the requested parameters in a short list. Structured prompting guidance from Google recommends clear delimiters, explicit parameters, and consistent organization. These choices reduce ambiguity about which text is an instruction and which text is evidence.
Example for a calculation
First, list every numeric input and its source row. Second, state the formula and any assumption about units. Third, calculate each intermediate value and show the arithmetic. Fourth, give the result rounded to two decimal places. If an input is missing or inconsistent, stop and identify it instead of estimating.For planning tasks, replace arithmetic with milestones, dependencies, and unresolved decisions. The principle is the same: expose the reasoning inputs before requesting polished prose.
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Ask the model to audit its draft against the supplied sources after writing it, then have a person inspect the original sources for consequential work. Verification is a separate task, not a request to repeat the same answer more confidently.
Use a claim-by-claim audit
- Extract every factual, numerical, attributed, or externally verifiable claim from the draft.
- For each claim, record the supporting source identifier and quotation, or mark it
UNSUPPORTED. - Check that the wording does not exceed the source’s date, geography, edition, or stated certainty.
- Recalculate numerical results and inspect units, rounding, and copied values.
- Remove, qualify, or retract claims that fail the check, then produce the clean answer and an uncertainty list.
Keep review independent for important decisions
OpenAI recommends verifying important information because ChatGPT can fabricate citations and other references. The Department of Energy likewise recommends human review. A reviewer should open the original source, not merely accept a citation-shaped string in the model’s output.
Ask for a compact audit trail
For routine work, request a table with columns for claim, source, support quotation, status, and reviewer note. Keep the audit with the final answer when the result may be reused, published, or used to make a consequential decision.
How should I iterate after the model makes an error?
Identify the exact error, provide the corrected source or constraint, and request a revised answer with a short change log. Concrete feedback is more useful than repeating “be more accurate.”
Describe the failure precisely
Quote the incorrect sentence, explain why it is wrong, and name the source or rule that controls the correction. Distinguish a factual error from a formatting problem, a missing assumption, or an interpretation that went beyond the evidence.
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Supply the correction as new evidence
Add the authoritative passage, corrected number, or scope limitation to the evidence pack. Tell the model whether the correction supersedes an earlier item or merely narrows it. This prevents the old and new versions from being blended.
Request a change log
Ask for three items: the revised answer, a list of changed claims, and any remaining uncertainty. The change log makes later review traceable and reveals whether the model fixed the root cause or only rephrased the sentence.
Preserve useful history
For recurring work, keep the original prompt, evidence version, correction, and final output together. The record helps you improve the contract and spot repeated failure patterns without relying on memory.
What reusable prompt combines all six tactics?
The following template turns the tactics into one repeatable workflow. Replace the bracketed fields, paste the evidence between the delimiters, and adapt the output requirements to your task.
You are answering [task] for [audience]. Use only the information in [provided sources/context] for factual claims.
Scope and output:
- Include [required topics or fields].
- Use [format, headings, table columns, units, and length].
- Exclude [out-of-scope material].
Evidence and uncertainty:
- Attach a source identifier or quotation to every material factual claim.
- Label each statement FACT, INFERENCE, ASSUMPTION, or UNSUPPORTED.
- If evidence is missing, say “I don’t know from the supplied information.”
- Do not invent quotations, studies, citations, numbers, or references.
Work in steps:
1. List assumptions, inputs, and missing information.
2. Map claims or calculations to the supplied sources.
3. Draft the answer in the requested format.
4. Check every claim against the sources, retract unsupported claims, and return an uncertainty list.
Sources:
<source id="S1">
[paste source text]
</source>For a high-consequence task, add a human-review instruction: “Before use, a person must inspect the original sources for every material claim.” For a quick, low-risk task, you can omit the full audit table while retaining the uncertainty rule.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How much effort should I spend for different tasks?
Match the workflow to the cost of being wrong. The following framework uses control-versus-effort trade-offs rather than pretending that one prompt fits every situation.
Low-risk drafting
For brainstorming or a private first draft, define the audience, scope, and format, and allow the model to flag missing information. Treat the output as a starting point, not as verified fact.
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Supply the relevant sources, require claim-level citations, separate facts from inferences, and run a claim audit before sharing. This is appropriate when readers may rely on the result but an error is recoverable.
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High-risk decisions
Use the complete sequence: evidence pack, explicit uncertainty labels, decomposition, verification, and independent human review of primary or trusted sources. Preserve the audit trail and do not allow an unsupported answer merely because a deadline is tight.
What does the available evidence say about these tactics?
Google Research reported “over 30% relative improvements” for its AGREE grounding framework across five datasets in 2024. That is a result for an adapted research framework under its reported conditions, not a guaranteed improvement from ordinary prompting or from any single tactic in this guide.
The broader lesson is consistent across the guidance from OpenAI, Anthropic, the U.S. Department of Energy, and Google: grounding, explicit uncertainty, structured steps, and review improve traceability and reduce opportunities for unsupported claims. None of those sources supports a promise that hallucinations can be eliminated.
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- “Be accurate” with no constraints: replace the slogan with a source set, output contract, and failure rule.
- Unbounded requests: define the date, jurisdiction, edition, or dataset so the model does not silently broaden the task.
- Evidence pasted without labels: identify each source and separate it from instructions.
- Forced certainty: permit “I don’t know,” ranges, conditional answers, and unsupported labels.
- One-pass complex work: request inputs, assumptions, intermediate checks, and unresolved questions first.
- Citation theater: inspect the cited passage or original source instead of trusting a realistic-looking reference.
- Vague correction requests: identify the exact wrong claim and provide the controlling correction.
These fixes do not make the model infallible. They make unsupported output visible early enough for you to correct it.
Frequently Asked Questions
Can these prompt tactics guarantee a hallucination-free answer?
No. They reduce risk and improve traceability, but a model can still misunderstand a source or make a new error. Treat the output as provisional until important claims have been checked against the original material.
Should I ask the model to cite every sentence?
Require support for every material factual claim rather than mechanically citing trivial connective language. A source identifier plus a short quotation or data reference is more useful than a long list of citations that no one verifies.
What should I do when two trusted sources disagree?
Ask the model to display both claims with their dates, scope, and exact supporting passages. Decide which source governs for your task, record that rule, and preserve the disagreement instead of allowing a silent blend.
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Keep a compact contract: state the task and audience, name the allowed source, require “I don’t know from the supplied information” for gaps, and ask for one quick claim check before use. This preserves the highest-value controls without a long multi-stage workflow.
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