Imagine you are checking homework.

A student writes the answer neatly.

Nice handwriting. Clean steps.

Box around the final number.

But the answer is wrong.

The neatness did not help.

That is what AI output often feels like.

It can look correct before it is correct.

Take a customer reply.

AI writes:

I understand your frustration.
We are working to resolve this as soon as possible.

Polite. But did it mention the actual bug?

Did it say what was fixed?

Did it give the customer one clear next step?

If not, the problem is not grammar.

The problem is that nobody checked the answer.

We usually try to fix this by improving the prompt.

That helps, but only up to a point. A better question does not remove the need to check the answer.

Machina's Hermes article says the quiet part clearly:

So today's shortcut is simple.

After AI writes, make it check the work.

write
check
fix
then ship

If AI can make the draft, it can also help inspect the draft.

You still decide.

The checklist just makes the mistakes easier to see.

I am Alex, welcome to ShortCu8 by Innov8.

Lets Dive Deep 🐰

Use this flow:

That is the basic eval loop.

You do not need to make it complicated on day one.

Start with one checklist.

Then use it every time before publishing, sending, or shipping AI work.

Prompting is input-side.

It controls what you ask.

But slop is often output-side.

It happens after the AI answers.

Example:

Write a LinkedIn post about AI agents.

You get a decent post.

Then you ask again tomorrow.

This time it sounds like every other AI post.

So you try:

Make it more human.
Make it less generic.
Use my voice.
Avoid AI slop.

That may help.

But it still does not guarantee quality.

Because the model can still produce a weak version.

The real question is:

How do I know this output is good before I send it?

That is where evals come in.

Do not start with a giant system.

Start with 10 good examples.

For content, this could be:

This is your gold standard.

It shows what "good" means for you.

Then write a simple rubric.

For our newsletter style, the rubric could be:

Now you have something better than vibes.

You have a standard.

Use a simple score from 0 to 1.

Example:

The judge can still miss things.

The point is the second layer:

one more chance to catch the weak run before it leaves.

Instead of asking:

Does this feel good?

You ask:

Where did it fail?

That is a better question.

This is the easy part to skip.

When an AI output fails, do not just fix that one output.

Save the failure.

Example:

Failure:
The article opened with broad AI hype before saying anything useful.

New rule:
Do not open with broad AI hype.
Start with a concrete scene, mistake, or use case.

Now the next output can be checked against that rule.

Every failure should make the system harder to fool next time.

That is how the quality floor rises.

Not from one perfect prompt.

From a loop.

You can do the beginner version manually.

A saved prompt is enough.

Hermes becomes useful when you want the loop to run repeatedly.

Based on the official Hermes docs, it has a few pieces that fit this:

So the Hermes version looks like this:

1. Save your gold examples.
2. Create a scoring skill.
3. Run every draft through the scoring skill.
4. Block anything below 0.7.
5. Save failures as new rules.
6. Use goals or scheduled checks when the task repeats.

That is where Hermes becomes useful.

Copy this:

Use this on your next AI draft.

The upgrade is not:

better prompt

The upgrade is:

prompt + quality gate

That is how you stop AI slop before it reaches people.

The model can write.The loop checks.

You decide. That is the system we want.

Now go create something great.

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