Every AI product now calls itself an agent, and most of them are chatbots with a new label. The technical distinction is real, it matters for what can go wrong, and it takes two minutes to understand.
The same AI model treats your data completely differently depending on which door you walk in through. Consumer chats may train future models by default now. Business and API traffic usually doesn’t. Most people have no idea which side they’re on.
Free AI tiers are genuinely useful, but they aren’t free. You pay in weaker models, tighter limits, and on consumer plans, in conversations that may become training data. Here’s how to tell when the paid plan actually earns its price.
Most AI tool trials are three impressive demos followed by a subscription. A real trial takes one week, uses your actual work, and produces evidence instead of a feeling. Here’s the exact method, step by step.
The real price of an AI tool isn’t the subscription. It’s how expensive it becomes to leave once your history, workflows, and habits live inside it. Here’s where lock-in actually comes from, and how to check the exits before you need them.
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.