Why AI Sometimes Delivers "Wow" — and Sometimes "Trash": How Generation Logic Works and Where Errors Come From

Before moving on to the practical part and building a consistent AI persona, it's important to understand several basic principles of how neural networks work. These principles directly affect what we get as output: why the same prompt sometimes produces perfect results and sometimes produces strange, illogical, or simply not what we wanted.

In this lesson, we'll understand how a neural network "thinks", why it tends to "fill in" missing details, how the training dataset affects results, and what can be done to increase generation predictability.

1) Neural Networks Don't "Calculate" or "Know" — They Guess the Most Probable

Imagine a simple question: Can a neural network answer that 2 + 2 = 5?

Yes, it can.

This is a good example because it demonstrates a key point: a neural network doesn't perform calculations like a calculator. It doesn't verify the truthfulness of an answer. Instead, it selects the most probable continuation of text or the most probable result based on what it was trained on.

If incorrect or "noisy" examples frequently appeared in the training data (or contexts where 2+2 is associated with 5), the network might decide that such an answer is probable.

From this, the conclusion: a neural network is a probability system.

It outputs not "truth," but the most probable result for a given query.

2) When Data Is Missing — The Network Starts "Inventing"

The next important idea: when a neural network lacks information, it doesn't say "I don't know." Instead, it tries to fill the gaps — invents missing details to make everything look plausible.

This manifests especially vividly in image generation. If we didn't specify important characteristics, the network will rely on what it considers normal — that is, the most typical image from its training base.

3) The Network's "Norm" Depends on What Data It Was Trained On

An example with the General (Safe / Not Safe Work) model: we ask to generate "a woman" — and get a certain type. From the result, we can reasonably assume that the model's dataset frequently featured women of Asian appearance aged approximately 18–25 years.

This doesn't mean the model "can't" draw other people — but it means that by default it will lean toward these parameters if we don't specify otherwise.

4) What Happens If Important Details Aren't Specified

If we don't specify breast size, the network substitutes the "norm" from training data. Conditionally, "by default," a 35-year-old European woman in the model's representation might be a woman with approximately size 2 breasts.

The same logic works with any details:

• Clothing (green top, dress, lingerie)
• Hairstyle
• Makeup
• Accessories
• Skin texture
• Age characteristics

If something is critical for us — it should be in the query; otherwise, the network will "invent."

5) Why NSFW Details Often Turn Out Poorly Without a Reference

If the network wasn't trained (or was trained with heavy censorship) on sufficient examples of nude bodies, it won't "understand" anatomy in detail. As a result, typical artifacts appear:

• "Symbolic" nipples (just darkening instead of form)
• Strange breast geometry
• Unnatural light and texture transitions
• Anatomical errors

Final Formula

To get predictable and quality results, we do three things:

1. Describe the prompt in detail and don't leave critical things "to guesswork."
2. Try different models, because each has its own dataset, style, and weak points.
3. Use additional control levers (references, LoRA) when precise details or consistency are needed.

This foundation is needed precisely for the practical part: later, when we start building a consistent character, we'll constantly face the network "normalizing" details if they're not anchored by input data.