AI & Software

Most AI output problems aren't model failures. They're prompt failures. Here's how to stop blaming the tool and fix the actual problem.

Most teams fine-tune a model expecting it to learn new information. That's not what happens. Here's what actually changes inside the model.

When a bug only appears in production, it's not bad luck. It's a signal that your test suite is modeling the wrong world.

Shipping a model isn't the finish line. It's where the interesting problems start. Here's what your model is silently doing (and suffering) in production.

Some bugs vanish the moment you look for them. That's not a coincidence — it's a signal about how your mental model of software is wrong.

Prompt engineering gets all the attention, but the real bottleneck is the flawed assumptions developers bring to every interaction with a language model.

When two services hold conflicting versions of the same fact, most teams treat it as a bug to fix. It's actually a design decision you already made, whether you knew it or not.

Your prompt worked perfectly yesterday and produces garbage today. The model didn't change. Here's what actually did.

The seconds between your prompt and a response aren't waiting time. They're a specific, traceable sequence of operations worth understanding.

Most teams treat LLM context windows like RAM and wonder why costs explode. Here's what's actually happening and how to fix it.

When AI models give conflicting answers to the same question, something real is happening under the hood. Here's what it actually means.

AI writing tools are getting better at finishing your sentences. That's exactly the problem.

Vector databases find 'nearest neighbors' using distance math, but distance and similarity are not the same thing. Here's where that gap causes real problems.

Prompts influence LLM outputs, but the real controls are baked in long before you type a word. Here's what actually shapes what you get.

A large context window sounds like a simple upgrade. The reality involves quadratic costs, attention decay, and some genuinely surprising tradeoffs.

Heisenbugs disappear when you try to observe them. Here's why they happen and how to actually catch them.

You probably think of embeddings as an AI feature. They're actually becoming foundational infrastructure, quietly running under search, recommendations, caching, and more.

Deleting your account doesn't mean your data disappears. Here's what actually happens to the conversations, fine-tuning data, and model weights you've contributed.