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You can reduce unsupported claims in AI-generated text by giving the model clear boundaries, supplying relevant evidence, checking each factual claim against its source, and testing the workflow on realistic examples. These steps lower risk; they cannot guarantee correctness. A detailed prompt can guide how a model responds, but it cannot make an unsupported fact true.
Why better prompts help—but cannot guarantee accuracy
A prompt can define the task, audience, scope, format, and how to handle missing evidence. That makes the model’s expectations clearer, but generated responses remain non-deterministic. OpenAI describes prompt engineering as writing instructions intended to produce outputs that consistently meet requirements, and recommends evaluating prompt behavior rather than assuming a wording will work in every case. For production applications where consistency matters, OpenAI also recommends pinning a specific model snapshot. See the OpenAI prompt engineering guide.
Use prompts to control the process, not as a substitute for evidence. If a question depends on current, specialized, or organization-specific information, provide reliable source material or retrieve it for the model.
Write a prompt that sets an evidence boundary
Specify what the model should produce and what it may rely on. Keep source material distinct from instructions, especially when you need to prevent outside assumptions from being presented as established facts.
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- Task: Say what the model should do, such as summarize a policy or answer a question from supplied documents.
- Audience and scope: Identify who the answer is for and which topic, time period, region, or document set is in scope.
- Output shape: Request the format you need, such as a short answer with a list of supporting sources.
- Missing evidence: Tell the model to say when the provided material does not support an answer, rather than guessing.
- Traceability: When review matters, ask the model to identify which source supports each factual claim.
These are practical prompt-design recommendations, not a magic formula. Review the resulting answers against the evidence even when the model follows the requested format.
Ground answers in relevant source material
Grounding means giving the model relevant, verifiable information to use when answering. One common approach is retrieval-augmented generation (RAG): retrieve relevant information and add it to the model’s prompt. Google Cloud describes this pattern in its generative AI application guidance.
Grounding is useful when the answer depends on facts that may change, specialized material, or information specific to your organization. The model can only make good use of the material it receives, so check that retrieved sources are relevant, sufficiently complete, and up to date. Google Cloud’s generative AI documentation, last updated October 5, 2026 UTC, provides an entry point to its documentation on these topics.
Prompt-only versus retrieval-grounded workflows
| Consideration | Prompt-only workflow | Retrieval-grounded workflow |
|---|---|---|
| When it may fit | The answer does not depend on fresh or specialized facts, or the needed evidence is already included in the prompt. | The answer depends on current, specialized, or organization-specific facts that can be retrieved from relevant sources. |
| Source traceability | Ask the model to distinguish supplied facts from unsupported assumptions; verify any citations it gives. | Retrieved material can provide a basis for checking claims, but each claim still needs review against its source. |
| Missing or conflicting evidence | Instruct the model to state when the prompt does not provide enough support. | Check for gaps, stale material, or conflicting sources; retrieval does not resolve those problems automatically. |
| Implementation trade-off | Typically simpler when a small, stable evidence set can be supplied directly. | Requires a retrieval workflow and source-quality checks; the appropriate implementation depends on the use case. |
| Evaluation | Test realistic prompts and compare answers with known, supported facts. | Test both retrieval quality and the claims in generated answers against their sources. |
This comparison is a practical decision framework, not a guarantee that either workflow will be accurate. RAG adds relevant material to the model’s context; it does not ensure that the answer uses that material correctly.
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Check factual claims one by one
Do not treat a citation as proof that an entire sentence is supported. Compare each factual claim with the underlying source and look for details that the source does not establish. A statement can be partly correct yet still overreach in an important qualification.
Google Cloud’s grounding-check documentation says, “Perfect grounding requires that every claim in the answer candidate must be supported by one or more of the given facts.” In the described version of its tool, a sentence is treated as a claim and checked against cited fact chunks; partial entailment does not count as grounded. The documentation is explicit that a claim must be wholly supported, not merely related to a source. See Google Cloud’s grounding-check documentation.
- Separate the answer into factual claims. A sentence with several dates, causes, or qualifications may need more than one check.
- Find the source for each claim. Follow the citation to the original material, not just a summary or a generated reference.
- Check the full meaning. Confirm the source supports the subject, action, scope, date, and qualifications in the claim.
- Revise or remove unsupported detail. If the evidence supports only part of a sentence, narrow the statement or say the source does not establish the rest.
Google Cloud documents operational limits for that specific grounding-check API: it accepts up to 200 facts, with a maximum of 10,000 characters per fact, and an answer candidate of up to 4,096 tokens as defined on the documentation page. Its overall support score runs from 0 to 1. These are tool limits and score definitions, not a measure of how much hallucination a workflow prevents.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Evaluate prompts and models with realistic examples
A prompt that works on one question may fail on another. Build a set of representative examples for the kinds of requests your workflow will handle, paired with ideal answers or known, source-backed facts. Use the same set to compare outcomes after changing the prompt or model.
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- Include varied topics, wording, and levels of difficulty that reflect real use.
- Check factual support as well as whether the answer follows the requested scope and format.
- Use automated metrics where they help scale comparisons, but include human review for context and nuance.
- Repeat the evaluation after material prompt or model changes.
OpenAI recommends evaluation suites and pinned model snapshots for production consistency in its prompt engineering guide. Google Cloud likewise recommends diverse evaluation examples and human review alongside metrics, which may miss language nuance, in its application-development guidance.
Quick Recap
Common mistakes that undermine source checks
- Assuming precise instructions make facts true: Prompts shape responses, but they do not establish the truth of claims.
- Supplying irrelevant or incomplete sources: Retrieved evidence that does not answer the question cannot support a reliable answer.
- Checking only the citation’s presence: A cited source may support one detail but not the full sentence.
- Accepting a partly supported sentence: Keep or rewrite only what the source actually establishes.
- Relying on automated scores alone: Metrics can help compare outputs, but human review may catch context and nuance they miss.
- Skipping tests after changes: Re-evaluate when a prompt or model changes materially.
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