Models, assistants and the companies building them.
Every provider sells a big model and a small one, and most people default to the biggest without asking whether their tasks need it. Research on real workloads suggests the answer is no more often than the pricing pages imply.
RAG connects a language model to real, current documents before it answers a question. It cuts down on hallucination without retraining anything, though it comes with its own failure modes worth knowing about.
Temperature controls how predictable an AI model’s word choices are during generation. Setting it to zero feels like it should guarantee identical answers every time. It doesn’t, and the reason why is worth understanding.
Fine-tuning and prompting both aim to change how a model behaves, but they work in completely different ways. One retrains the model. The other just changes what you tell it. Here’s how to actually choose between them.
A high MMLU score sounds like proof a model genuinely understands its subject. Often it’s closer to proof the model has seen a version of the test already. Here’s what a benchmark score really tells you and where it misleads.
Meta calls Llama open source. The organization that has defined that term since 1998 disagrees. The gap between open-weight and genuinely open-source AI is a real, substantive difference, and it has real consequences for what you’re allowed to do with a model.
Context windows now reach millions of tokens, but bigger doesn’t mean the model actually remembers more. Here’s what the number really tells you.
AI detectors misclassify real human writing far more often than most people realize, and the mistakes aren’t random. Here’s the mechanism behind it.
Hallucination isn’t a bug that better training eventually fixes. OpenAI’s own research suggests it’s a statistical inevitability. Here’s why.
Prompt injection is ranked the top security risk for AI applications by OWASP. Here’s how the attack actually works, and why no model fully prevents it.