LLMs are very useful, but I don’t think we should trust their output right away.
Not because they are bad. More because they are flexible.
They can write a nice answer. They can explain something well. They can help with messy input. But they can also return something that looks right and still has a problem inside.
This is the tricky part.
A normal error is easy to notice. The request fails. The code crashes. The field is missing. The database rejects the value.
But with LLMs, the output can look clean. It can have the right tone. It can even follow most of the instructions. And still, one small part can be wrong.
Maybe the JSON is almost valid, but not exactly.
Maybe the answer uses a value we never allowed.
Maybe the model made a choice without enough data.
Maybe it says “yes” when the safe answer should be “I don’t know”.
This is why validation layers matter.
For me, a model output is not the end result. It is just another input.
And input should be checked.
I like to think about it the same way as user input from a form. We do not trust a form just because it looks simple. We check required fields. We check types. We check limits. We check permissions. We check if the value makes sense for this user and this action.
LLM output needs the same care.
A good validation layer can ask simple questions:
- Is the format correct?
- Are all required fields there?
- Are the values allowed?
- Is the answer too long?
- Did the model include something it should not include?
- Does this match the source data?
- Is this action safe to run automatically?
These checks do not need to be fancy. Sometimes the best checks are boring.
A schema. A list of allowed values. A confidence rule. A second step that compares the answer with real data. A fallback when the output is not good enough.
This makes the system feel much safer.
It also makes it easier to improve. When validation fails, we learn something. Maybe the prompt needs to be clearer. Maybe the schema is too loose. Maybe we need better examples. Maybe this task should not be fully automated.
Without validation, all of these problems reach the user.
And the user does not care that “the model made a mistake”. They only see that the product did something wrong.
I think validation is not about making AI less powerful. It is about giving it a safe place to work.
The model can still be creative where we need creativity. It can still help with language and messy data. But the system around it should decide what is allowed, what is trusted, and what needs review.
That is the difference between a demo and a real product.
A demo can work when everything goes well.
A real product needs to know what to do when things go a little wrong.