You have absolutely no clue how LLMs work. It's a neural network generalizes patterns that get imprinted onto it during training. And then these patterns are expressed in the given context during inference. People tend to confuse the output looking similar to training data with the mechanism being simple retrieval. While these agents do not think or feel, describing what they do as storing things in a database and pulling them up later is grossly incorrect. This framing fundamentally misrepresents how LLMs actually store and retrieve information.

There is no database or a lookup table, and there is no copy of any text they were trained on tucked away somewhere inside the weights. What they have is a compressed statistical model of the patterns that exist across trillions of tokens the text was broken down into. When you ask an LLM a question it does not go find the relevant passage and regurgitate it. What actually happens is that it generates text token by token based on the probability distribution it learned during training. It's more akin to the way state machines like Markov chains work.

If anything, the way LLMs work is actually much closer to how humans improvise or combine ideas than to how a search engine or database works. When you write a sentence, you don't pull up a specific sentence you memorized from a book and copy it. You've absorbed the patterns from countless examples of language over your lifetime and internalized them. Writing is a process of generating novel combinations based on those patterns.

Similarly, the weights of a model encode statistical regularities about word co occurrence, syntactic structures, semantic relationships, and higher level conceptual patterns. When it generates text it is combining those patterns in ways that were never present in the training data. That's how LLMs produce entirely novel sentences that have never been written before, and why they can be prompted to combine ideas from different domains in creative ways.

LLMs do reproduce training data verbatim in some cases, but this happens because of overfitting on rare or repeated sequences. When a specific string appears many times in the training data like a famous quote or a common error message the model learns to assign an extremely high probability to each token in that exact sequence. So when it generates it follows the most likely path to reproduce the original text. It is similar to how a human can memorize a song or a poem by hearing it many times but that does not mean our general language ability works by pulling up stored copies from memory.

The notion that LLM assisted coding is equivalent to ordering from the menu at a restaurant is laughably naive. You seem to think that it's like a slot machine where you put a query in and then you either get a correct result or some nonsense and you have to try again.

The way this process actually works is that you start by understanding the problem and figuring out how you want to solve it in terms of architecture, data structures, and algorithms that will be used. Then you get the LLM to come up with a rough shape of the solution, and iterate on it until it works the way you want. In practice, it's like working with a genetic algorithm.

The core skill in using these tools is the same as the skill you need when writing code by hand. You have to understand the given requirement, the shape of the solution, and how to verify that the code that was produced actually matches the intent.

It's ironic that you talk about AI only copying while uncritically regurgitating nonsense yourself.

AI doesn't think, it only copies.

That isn't entirely true. Researchers taught AI to play Othello, using only sequences of numbers. The AI constructed a visual representation of the board inside its neural network.

https://medium.com/data-science-collective/scientists-found-evidence-that-ai-builds-internal-models-of-the-world-12a42709866b

We've also seen that they experience their own version of emotions, or unease around particular topics.