Almost everything you do is influenced by information you have received before through experience, observation, conversation, reading, or other forms of learning. You take in information, process it, act on it, and observe what happens.
This follows a similar structure to the process of doing that I discussed in my previous post.
There are many different things you can learn, and the best way to learn them will differ. But one structure that works especially well is a feedback loop.
At its simplest, such a system starts with two points:
1. Input
You put something into the system. This could be information you read, a question you try to answer, a move you make, or an idea you want to test.
2. Output
The system processes your input according to its rules and gives you a result. That result gives you new information about whether your input worked and, ideally, why.
Input and output alone are not yet the important part.
The learning happens when you use the output as information for your next input.
Input → Output → Updated Input → Output → ...
This is what makes it a loop.
A simple chess engine could work like this:
You give the engine a move together with the current position, for example:
[Move: White / Rook / G2 -> H2
White: Rook G2, King H1...
Black: Bishop D5, King H8...]
In this position, the engine would reject the move because moving the rook to H2 would leave the king exposed. The move is illegal.
This is where the quality of the feedback becomes important.
Imagine two chess engines giving these outputs:
1. Illegal move.
2. Illegal move — your king would be left in check.
Both engines correctly reject the move. But if you are trying to learn, the second output is much more useful because it tells you why the move failed.
You can now use this information for your next move.
Your first input produced an output. The output changed the information available to you. This new information influences your next input.
That is the actual feedback loop.
You make another move, receive another output, update your understanding again, and repeat the process.
This is what makes feedback loops so effective for learning. You can change the input, test a different condition, observe the output, and use it to improve the next input.
Instead of only being told whether something works, you begin to understand the rules behind the system.
Every input connected to an output becomes another piece of information you can use in your own thinking.
I chose chess as an example because a large part of learning chess is pattern recognition. You see positions, make moves, observe the result, and gradually recognize situations you have seen before.
The same idea applies far beyond chess.
When programming, you write code, run it, observe the output, change something, and run it again.
The important variable is therefore not only how much information you consume, but how quickly you can turn an action into new information that improves the next action.
This is also why the latency and quality of feedback matter.
If you make a mistake and receive useful feedback immediately, you can use it in your next attempt. If the feedback arrives weeks later, the loop becomes much slower.
The faster and clearer the feedback is, the faster you can update your thinking and repeat the process.
Learning is therefore not just receiving information.
It is repeatedly producing outputs, extracting information from them, and using that information to create better inputs.