We’ve all seen the memes about AI confidently recommending something or giving an answer that turns out to be completely false. Those of us who work with AI have experienced our own versions: a fact that does not exist, an invented source, a response that misses the question, or an explanation delivered with more confidence than the evidence deserves.
I have spent hours following a process AI suggested, only to discover near the end that the process would not actually work. The system had reasoned toward what seemed as though it should be possible instead of recognizing what it could not do. Experiences like that have been frustrating, but they have also been some of my best lessons in using AI. Over the past eighteen months, I have learned to write better prompts, supply clearer context, choose tools more carefully, and recognize where verification is necessary. As the quality of my results improved, something else changed as well: I became less willing to assume that a polished answer deserved my trust.
What You Need to Know to Review AI
Responsible review begins with knowing what you actually asked the system to do and noticing when it has quietly changed the assignment. A request to organize information can become interpretation. A request for possibilities can begin sounding like a recommendation. Source discovery can turn into synthesis that goes beyond what the underlying material actually establishes.
The development of this article gave me a useful example. I supplied a rough opening because I wanted to establish the direction of the argument. In one sentence I referred to stories many of us have read about AI and critical thinking. The response treated that sentence as though I had made a settled empirical claim and began correcting it. The correction sounded careful and responsible, but it was correcting something I had not actually said.
Later, AI proposed an outline section centered on the idea that having a human reviewer is not enough. The research supports that point, yet it was not quite the article I wanted to write. My concern was more practical: what does a person need to know in order to review AI well? Recognizing that shift required more than checking grammar or factual accuracy. I needed to understand the research, know what I meant, and have enough command of the article’s argument to see that a reasonable response was still taking the work in the wrong direction.
You Do Not Need to Be an AI Engineer
The competence required for that kind of review is practical. I do not need to understand the mathematics behind a large language model, but I do need enough familiarity with the systems I use to recognize their characteristic strengths and weaknesses. Prompting and context affect the response. Different tools are better suited to different tasks. A confident answer may still need to be checked against evidence.
Just as important, AI competence cannot replace knowledge of the work itself. If I use AI to assist with accounting, governance, theology, law, or another consequential field, I still need enough subject knowledge to evaluate what it gives me. Part of competence is knowing where my own knowledge ends. When I cannot responsibly judge an answer, the solution is not simply a better prompt. I may need stronger evidence or someone with expertise I do not possess.
Why Use AI If It Requires This Much Oversight?
All of this can make AI sound like more work than it is worth. In practice, responsible oversight does not require me to recreate everything the system has already done before I can use any of it.
AI can help me work through a large amount of information, expose weaknesses in an argument, suggest a structure I had not considered, or give me useful possibilities to evaluate. It often accelerates familiar work, but its value is not only speed. It can broaden what I am able to examine by helping me compare ideas, question assumptions, and work through material that would otherwise take considerably longer to organize.
The amount of attention I give the output depends on what is at stake. If AI reorganizes material I already understand, I can often tell quickly whether the result is useful. A grammar change or brainstorming suggestion does not need the same scrutiny as a claim that could affect money, employment, governance, or legal responsibility. Learning to use AI well has therefore made me more selective, not more suspicious of everything it produces. I can move quickly where the consequences are small and slow down where evidence and judgment matter more.
The Hardest Errors Are the Ones That Sound Right
Obvious hallucinations can be irritating, but they are often easier to catch. A citation that leads nowhere or a response that clearly misunderstands the request gives me an immediate reason to stop. The more difficult problems are plausible enough to pass unnoticed.
AI can describe a real source inaccurately or leave out information that would change the conclusion. It may pick up an assumption from my prompt and build an entire explanation around it. At other times, it creates an account that hangs together beautifully even though the evidence does not really support it.
That is why a citation from AI is an invitation to inspect the source rather than proof that the claim is sound. Even agreement between AI systems does not necessarily settle the matter. When the issue is consequential, I may need to compare what the system says with the underlying material and determine whether the evidence actually carries the conclusion being offered.
What Happens to Critical Thinking?
We’ve all read stories asking whether AI is weakening critical thinking. There are legitimate reasons for concern. Some research has found an association between greater confidence in AI and lower self-reported critical-thinking effort, while other work has raised questions about what people retain when AI performs much of the work for them.
That evidence does not lead me to conclude that frequent AI use automatically makes someone a poorer thinker. The more useful distinction concerns what the person is asking the system to carry. AI can supply an answer before someone has seriously engaged the problem, but it can also be used to challenge a conclusion, compare perspectives, expose assumptions, or test an argument.
That matters to the way I work. I want AI to expand what I can examine without becoming the place where my own understanding ends. If I allow it to frame an issue before I have thought about the issue myself, or accept a conclusion because working through it would take more effort, I gradually become less capable of providing the oversight I claim to provide.
The More Consequential the Work, the More Competent the Oversight Must Be
Church leaders already make these kinds of distinctions in other parts of their work. Editing a congregational announcement carries different consequences from interpreting bylaws, evaluating financial information, handling personnel matters, or explaining denominational polity. AI does not erase those differences simply because the same interface can be used for all of them.
As the consequences rise, so does the need to know why an answer should be trusted. Sometimes I can make that judgment from my own knowledge. In other situations, I need to return to the governing document or underlying source. A legal, accounting, or other professional question may require someone qualified in that field rather than continued conversation with AI.
Competence also concerns what goes into the system. A useful answer is never sufficient reason to expose confidential pastoral information, personnel records, financial credentials, or other material that should have remained protected. Responsible use begins before the answer appears on the screen.
Knowing How to Use AI Includes Knowing When Not to Use It
Greater familiarity with AI has made me more comfortable using it widely while also making the limits of that use clearer. There are times when I can proceed with ordinary review, and there are times when I need to check the evidence directly, use a different tool, or bring another person into the work. Occasionally the right conclusion is that AI does not belong in the task at all.
The hours I spent in rabbit holes were frustrating because I wanted the tool to work. Looking back, many of those experiences became part of my education. They taught me to recognize where the system was genuinely extending my capacity and where it was improvising beyond what I should trust. They also taught me how often my own expectations were part of the problem.
AI systems will continue to change, and some of today’s limitations will undoubtedly look different a year from now. That makes continued learning part of the responsibility I assume when I use them. If AI is going to participate in consequential work, I need enough understanding to recognize when it is helping, when the evidence needs another look, and when the responsibility in front of me requires judgment the system cannot supply.