Many companies say they use human-in-the-loop (HITL) to review AI output, and they say it in a way that says, “We’re not like the others.”
There is a good reason for this. Even the most gung-ho AI user has to admit that LLM-based systems can be, at best, inventive and, at worst, wrong. But are humans the best at evaluating product suitability?
HITL is often used in a very informal way – once a result or piece of content has been produced, someone looks at it and makes a decision on the acceptability of the work. In short, it is tested for usability. There is an obvious problem with that: Transparency is not accuracy.
And the weakness of HITL is that LLMs are trained to develop for soundness, not accuracy. Consider all news stories about legal summaries full of citations. The quotes were fake, but, boy, did they look real.
The solution is Bayesian reasoning, which provides a robust framework for evaluating AI output.
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What is Bayesian inference?
Bayesian inference is a probabilistic approach. It took centuries to develop because it depended on something traditional mathematicians did: starting with a belief.
To understand how it works, consider checking whether a coin is correct.
- The traditional (frequentist) method: You start without thinking. You flip the coin 1,000 times, analyze the raw data, and decide if the coin is worth it based on those results.
- Bayesian method: You start with an assumption (“preconceived belief”) based on what you already know. If a coin looks normal, you think it’s worth it. If a transsexual stranger offers it to you, you may be skeptical. Then, you start scrolling. With every flip, you revise your belief using new evidence.
For a long time, critics argued that presenting the first belief in mathematics was too narrow and unscientific. But then the Internet came along, flooding us with messy, unstructured data. We didn’t have the time or controlled conditions to do formal experiments — we had to make quick decisions with incomplete evidence.
Almost overnight, the philosophical debate disappeared. Bayesian methods now power everything from modern spam filters and search algorithms to predictive marketing tools—because they work.
Why this is important for AI
If you treat AI like an oracle, you think its output is the final, irrefutable truth. If you treat it as a Bayesian evidence generator:
- You bring up the previous belief: You start with your context, domain expertise, and expectation base.
- Measure the output as evidence: You evaluate AI feedback as new information—not a complete decision.
- Updating your location: You adjust your opinion based on how persuasive, logical, and factual the AI’s response is.
Instead of blindly trusting AI or dismissing it when it sees things wrong, the Bayesian approach helps you treat AI-generated insights for what they really are: a piece of evidence that you can use to refine your decision-making.
How to put Bayesian reasoning into practice
Here’s how to use it:
Clarify your preconceived notions before using the tools
Find your beliefs about the topic you are discussing. What are your product guidelines? What makes people react to slop? Who is this for, and what do they care about? What do you know, and what other sources of truth do you trust in addition to the LLM?
Treat AI as evidence, not reality
If it suggests a decision or a way forward, treat it as evidence that you align with what you already know and believe. Given a tool, how much weight do we put on this – is this wisdom, or just what 100 users on Reddit thought? Do the sources mean what the LLM thinks they say?
Make the loop in HITL a Bayesian loop
Rather than simply accepting and saying, “Tell me more” or “Good, but too blue,” include additional evidence based on your revised beliefs. Give the tool examples or challenge.
Enforce the use of decisive action tools
Where possible, don’t rely on LLMs to do things that other tools do reliably. It might sound easy to just upload a .csv file and ask it to write a report, but if you do, you’ll spend your entire day checking the results (or – worse – sending them to the client without looking at them).
Put human judgment in the center
The true way to test a persona is not about error checking or checking if it happens, but about bringing all your human essence and understanding to your product, your customers, and your audience, and testing it against the new evidence that these amazing (and sometimes intoxicating) tools reveal. Working this way really elevates you from box-ticker to master.
