AI tools can invent citations, misstate facts and sound confident while doing it. A practical verification routine for anyone using AI in legal or case work.
Courts in several jurisdictions have sanctioned attorneys for filing briefs that cited cases that did not exist. The citations looked real. The quotes sounded plausible. They had been generated by AI tools and never checked. These incidents became cautionary tales for a simple reason: AI systems can produce confident, polished output that is partly or entirely wrong.
For anyone using AI in legal, investigative or case work, verification is not optional. It is the core skill. This guide explains the common ways AI output goes wrong and offers a practical routine for checking it. It is general information; follow your organization’s policies and professional obligations.
How AI output goes wrong
Common problems include:
- Fabrication: invented facts, quotes, statistics, cases or sources that look real
- Misreading: getting details wrong from the input, like confusing names or dates
- Omission: leaving out important information, especially details that contradict a pattern
- Overconfidence: stating uncertain conclusions as facts
- Outdated information: describing laws or practices that have changed
- Bias: reflecting patterns in training data that disadvantage certain groups
- Drift: gradually moving away from the source material in long outputs
These problems can appear even in excellent tools, and they are hard to spot because the writing quality is usually high.
The verification mindset
Treat AI output like a draft from a capable but unreliable assistant who sometimes makes things up. Everything that matters gets checked. The more consequential the use, the more thorough the check.
Step one: check every citation
If an output cites a case, statute, regulation, article or other source, look it up in an authoritative database. Confirm that:
- The source exists
- The citation is correct
- The source says what the output claims
- The source is current and still good law, where relevant
Never file, submit or rely on a citation you have not personally verified.
Step two: check facts against the source material
When an AI tool summarizes documents, compare key facts in the summary with the originals: names, dates, times, quantities, quotes and sequences. Spot-check a sample of claims, and check every claim that matters to a decision.
Step three: look for what is missing
Omissions are harder to detect than errors. Ask:
- Does the summary include contradictory evidence?
- Are weaknesses and gaps mentioned?
- Are all relevant people and events included?
- Did the tool skip complex or ambiguous sections?
In case work, omissions can be as harmful as errors, particularly omissions of exculpatory information.
Step four: separate fact from inference
Identify statements that are interpretations rather than facts, and check whether they are supported. AI tools often blend the two smoothly. Rewrite or label inferences clearly.
Step five: check for bias
Look at how people are described. Are descriptions neutral and supported by evidence? Are assumptions being made based on demographics, neighborhoods or other factors? AI tools can reflect biases in their training data, and those biases can shape how a case is perceived.
Step six: check currency
Laws, rules, procedures and best practices change. If an output describes legal requirements or procedures, confirm them with current, authoritative sources.
Step seven: document your review
For important work, keep a record of what was checked and how. Some organizations require disclosure of AI use in certain documents; some courts have issued rules or standing orders about AI-assisted filings. Know the requirements that apply to you.
Prompts that reduce errors
Good prompts can reduce, but not eliminate, errors. Useful techniques include:
- Asking the tool to quote or reference the specific source text for each claim
- Asking it to state uncertainty explicitly
- Instructing it not to invent sources and to say when it does not know
- Requesting a list of assumptions it made
- Breaking large tasks into smaller, checkable pieces
Even with these techniques, verification remains essential.
Building a team verification routine
Teams that use AI should agree on verification standards:
- Which outputs require full verification and which require spot checks
- Who reviews AI-assisted work before it is finalized
- How citations are checked
- How AI use is documented
- Training for new staff
Consistent routines catch errors that individuals miss.
A quick verification checklist
- Every citation verified in an authoritative source
- Key facts checked against originals
- Omissions considered, especially contradictory evidence
- Inferences labeled
- Descriptions checked for bias
- Legal and procedural information confirmed as current
- Review documented where required
Questions case teams ask
If a tool is accurate most of the time, do I still need to check everything?
For anything that matters, yes. Errors are unpredictable, and the consequences in legal and case work can be serious.
Are specialized legal AI tools more reliable?
Tools connected to authoritative databases may reduce some errors, but verification is still required.
Do I need to disclose AI use?
That depends on your jurisdiction, court rules and organization. Check applicable requirements.
Key takeaways
Assume AI output can be wrong, verify every citation, check facts against sources, look for omissions, separate fact from inference, watch for bias, confirm currency and document your review. Responsibility stays with the human.
A worked example with a fictional summary
Suppose an AI tool approved for case data produces a summary of a fictional theft file. The summary states: “Witness Ana Torres saw the suspect leave the store at 3:15 p.m. carrying a black backpack. Video confirms the suspect’s exit. The suspect has a prior conviction for shoplifting.”
A careful reviewer checks each claim against the file:
- The witness statement says Torres saw “a man” leave “around three,” carrying “a dark bag.” The summary has added precision and turned “a man” into “the suspect.”
- The video shows a person exiting at 3:17 p.m. by the camera clock, but the face is not visible. “Confirms the suspect’s exit” overstates what the video shows.
- Nothing in the file mentions a prior conviction. The claim appears to be fabricated, or drawn from something outside the file.
Three short sentences contained three significant problems, and all of them made the case look stronger than the evidence supports. That pattern is common, which is why verification needs to be systematic rather than casual.
Verification costs time, and that is the point
Teams sometimes adopt AI tools expecting large time savings, then find that careful verification takes longer than they planned. That is not a sign the tools are useless. It is a sign that the team is using them responsibly. The realistic benefit of AI in case work is often a faster first draft and better organization, with the time saved partly reinvested in checking.
When estimating the value of a tool, include the cost of verification in the calculation. A tool that produces drafts needing heavy correction may save less time than one that produces shorter, more conservative drafts with clear source references. Measure on realistic fictional cases before rolling a tool out broadly.
Red flags that call for extra scrutiny
- Very specific numbers, times or quotes that you do not remember seeing in the source
- Citations to cases or statutes with unusual names or numbers
- Conclusions about intent, credibility or character
- Summaries that contain no uncertainty at all
- Output that sounds like it came from a different case or jurisdiction
- Smooth narratives built from fragmented or conflicting sources
When any of these appear, slow down and trace every relevant claim back to its source before going further.
When to stop using a tool for a task
If a tool repeatedly produces errors on a particular kind of task, such as summarizing handwritten notes or interpreting technical lab reports, it may simply be the wrong tool for that job. Document the pattern, share it with whoever manages the tool and consider restricting its use for that task. Recognizing a tool’s limits is part of using it well.
Building a shared error log
Teams that use AI tools regularly benefit from keeping a simple, shared log of errors they catch: the type of task, what the tool got wrong and how the error was spotted. Over a few months, patterns emerge. Perhaps a tool consistently struggles with dates in a certain format, or tends to invent detail when summarizing short documents. Those patterns can be turned into specific verification steps and prompt adjustments, and they make excellent training material for new staff, using fictional or redacted examples.
The log also helps with conversations about which tools to adopt, renew or retire. Decisions grounded in documented experience are more defensible than decisions based on impressions or vendor claims.
Verification as a professional habit
Ultimately, verification is less about any specific technique than about a mindset. Professionals in legal and case work already verify information from human sources, such as witnesses, informants and other agencies, before relying on it. AI output deserves the same treatment. The tools are new, but the professional obligation to get the facts right is not.
Prompting for checkable output
One of the most effective ways to make verification easier is to ask for output that is designed to be checked: claims tied to specific sources, uncertainty stated plainly, assumptions listed and no invented references. That takes deliberate prompting. Teams that standardize their prompts this way spend less time hunting for errors and catch more of them.
General information only. Follow your agency’s policies on data handling and approved tools, and never place confidential case material in a tool that has not been approved for it. Caseprep does not provide legal advice.