Outstanding framing of how AI models actualy work under the hood. The analogy to Joel Spolsky's Unicode essay is spot on, this really is that generation-defining piece about developer fundamentals.
What stands out most is your distinction between pattern matching and reasoning. Too many devlelopers treat these tools as if they're executing deterministic logic, when in reality they're just performing incredibly sophisticated next-token prediction. That mental model shift is crucial. The example with the three different email validation functions perfectly illustrates why you can't rely on prompt reproducibility the same way you rely on code reproducibility.
One insight that might extend this: the "lost in the middle" problem you describe with context windows maps directly to how teams should think about prompt architectre. Just like good software design emphasizes modularity and separation of concerns, effective AI prompting requires you to structure context so critical information sits at boundries where the model's attention naturally focuses. The instinct to dump everything into one massive context is like writing spaghetti code, it technically works until it doesn't.
Essential read! Understanding that AI coding assistants are pattern matchers, not reasoning engines, is the baseline for using them safely and effectively in production.
I talk about the latest AI trends and insights. If you’re interested in using AI coding assistants effectively while avoiding costly mistakes and understanding their limits, check out my Substack. I’m sure you’ll find it very relevant and relatable
Great article, couldn't have said it better myself. People really need to understand how LLM's work, and what is their "motivation" and how they "understand". It's all about math and statistical probabilities. Highly educated guessing!
The structure and formatting of this article feels heavily copy/pasted from LLM output (all the headings and bullets, things like "(The High-Level TL;DR)"). Somehow, for me, this cheapens the points the article is trying to make
Fantastic Article 👍
Outstanding framing of how AI models actualy work under the hood. The analogy to Joel Spolsky's Unicode essay is spot on, this really is that generation-defining piece about developer fundamentals.
What stands out most is your distinction between pattern matching and reasoning. Too many devlelopers treat these tools as if they're executing deterministic logic, when in reality they're just performing incredibly sophisticated next-token prediction. That mental model shift is crucial. The example with the three different email validation functions perfectly illustrates why you can't rely on prompt reproducibility the same way you rely on code reproducibility.
One insight that might extend this: the "lost in the middle" problem you describe with context windows maps directly to how teams should think about prompt architectre. Just like good software design emphasizes modularity and separation of concerns, effective AI prompting requires you to structure context so critical information sits at boundries where the model's attention naturally focuses. The instinct to dump everything into one massive context is like writing spaghetti code, it technically works until it doesn't.
Essential read! Understanding that AI coding assistants are pattern matchers, not reasoning engines, is the baseline for using them safely and effectively in production.
I talk about the latest AI trends and insights. If you’re interested in using AI coding assistants effectively while avoiding costly mistakes and understanding their limits, check out my Substack. I’m sure you’ll find it very relevant and relatable
Great article, couldn't have said it better myself. People really need to understand how LLM's work, and what is their "motivation" and how they "understand". It's all about math and statistical probabilities. Highly educated guessing!
The structure and formatting of this article feels heavily copy/pasted from LLM output (all the headings and bullets, things like "(The High-Level TL;DR)"). Somehow, for me, this cheapens the points the article is trying to make