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An almost-correct AI answer can be more costly than an obvious mistake because it may look credible enough to use while still requiring careful checking—or causing harm if its flaw goes unnoticed. That makes near misses a serious risk in tasks such as research, coding, and legal work. But the evidence available does not prove that they are the most expensive type of AI error overall, or put a universal dollar amount on their cost.
What counts as an almost-correct AI error?
“Almost correct” is a useful description, not a standardized error category. It means an answer that is plausible or substantially right but contains a mistake or omission that matters to the task. A wrong exam response, a code recommendation that does not work in a particular project, and a misleading legal citation are different kinds of failure; their risks depend on what someone does with the answer.
The key question is not how close an answer sounds to being right. It is whether its remaining flaw could change a decision, result, or action. A minor wording issue may be harmless in one context; a missing qualification or unsupported claim may be consequential in another.
Why can a near miss be harder to catch?
An obviously nonsensical answer is often easy to reject. A near miss can pass a quick read because much of it is coherent, familiar, or accurate. Finding the flaw may require checking source material, testing code in its intended environment, or confirming that a caveat has not been lost.
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That creates a plausible cost pathway: time spent validating, correcting, debugging, or delaying work, plus the risk of acting on an error that escapes review. These are reasons a near miss can be expensive in a particular task—not proof that it always costs more than an obvious error. The available studies do not provide a cross-industry comparison of dollar costs.
What does the evidence show?
A bounded study of AI grading
A 2024 study of 5,579 questions from 50 EPFL science, technology, engineering, and mathematics courses examined how GPT-4 answered questions and how well it graded them. Across prompting strategies under the study’s majority-vote setup, GPT-4 answered 65.8% correctly on average, and it could answer 85.1% of questions correctly under at least one of eight strategies. Those results describe performance in that specific educational dataset and setup, not a general accuracy rate for AI answers.
When human graders labeled examples “Almost Correct,” GPT-4 as a grader matched that category in 36% of cases. This is agreement with a particular human grading label—not evidence that 64% of generated answers were dangerously wrong, or a general estimate of how often AI makes near misses. Read the study in PNAS.
A small survey of Scrum practitioners
A study published in 2026 reporting a 2025 survey found that 81% of 49 Brazilian professionals who had recently worked on Scrum projects and used AI chat assistants in Scrum-related activities reported difficulty receiving output that was “almost correct but not quite.” The sample was self-reported and non-probabilistic, so the percentage should not be generalized to all workers or AI users. It is evidence that these respondents noticed the problem, not an audited rate of AI near misses across workplaces.
In the same survey, respondents also reported concerns about output variability (63%), privacy and confidentiality (63%), hallucinations (59%), and difficulty validating output (59%). These figures are survey responses, not measured incidence rates. Read the study.
Legal research illustrates why context matters
In legal research, a small wording or citation flaw can change the meaning of a claim. Jane Meland, assistant dean and library director at the John F. Schaefer Law Library at Michigan State University College of Law, writes that people using generative AI for legal research “will need to verify the results” and that “Reliable and accurate sources, such as the annotated code, will remain essential.”
Meland also quotes Nam Nguyen’s 2025 article on hallucinations in retrieval-augmented generation: “in law ‘almost correct’ is a liability, not an improvement. A single hallucination [or miswording] can turn an accurate statement … into a misleading one.” This is a professional observation about legal research, not a measured estimate of legal or financial costs. Read Meland’s article.
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Checking an answer means more than confirming that it includes a citation or sounds reasonable. For each important claim, examine the source and ask three questions:
- Faithfulness: Does the cited source support the exact claim being made?
- Completeness: Does the answer preserve the source’s relevant context and qualifications?
- Sufficiency: Is the evidence strong enough for the decision you need to make?
These are dimensions NIST uses in describing an ongoing project that evaluates agent factual claims against a human-curated corpus and records an audit trail. The project aims to move beyond “the AI said so” toward “here is what the AI found, where it found it, and how the evidence supports the conclusions.” It is an evaluation project, not proof that a commercial tool prevents errors or a guarantee that any one checking method will catch them. Read NIST’s project description.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What should you verify before using AI-generated research or code?
For factual claims and research
- Trace consequential claims to a primary or otherwise authoritative source.
- Check whether the source supports the answer’s precise wording, not just the general topic.
- Look for omitted context, conditions, dates, or qualifications that could alter the claim.
- Decide whether the evidence is adequate for the action you are considering; a citation alone does not establish that it is.
For code
- Run relevant tests and inspect what the code actually does.
- Check it in the project’s real environment, where dependencies, interfaces, and surrounding code can affect whether a recommendation works.
- Do not treat a confident explanation or a superficial check as proof of correctness.
These checks are practical ways to apply source-grounding and evaluation principles; the sources cited here do not establish a universal verification checklist or quantify how much a particular workflow reduces cost. NIST’s broader work on testing AI claims and traceable evidence is described in its AI Risk Management Framework Playbook.
Is “the most expensive type of AI error” a proven ranking?
No. The studies and project description discussed here support a narrower point: plausible errors can be difficult to validate, and context can make an undetected flaw consequential. They do not establish that near misses cost more than other AI failures across industries, provide aggregate monetary estimates for validation or rework, or measure whether verification practices reduce those costs.
A useful way to assess a near miss in a specific setting is to consider how easy it is to detect, how much work it takes to verify and repair, whether someone might act before discovering the flaw, and what the consequences would be if it remained unnoticed. This is a decision framework, not a measured ranking of error types.
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