Assessing whether and how an LLM is suitable for a task compared with simpler alternatives, using value, risk, evidence, and operational constraints.
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17 articles in this tag.
Here's the scene I've watched play out more times than I can count: someone builds their first agent, gives it a task, and watches it loop. It calls a…
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You have three tabs open. ChatGPT in one, Claude in another, Gemini in the third. You paste the same question into all three, and you get three different…
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That is the real beginner moment, and it does not get solved by reading one more "which AI is best" listicle. Those lists go stale the moment a new model…
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Most LLM projects don't die during the build. They die because someone picked a flashy demo and then went hunting for a problem it could solve. That's…
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You have a task that needs an LLM. The temptation is to reach for an agent—something autonomous, something that "figures it out." But most of the time, a…
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Most people picture "AI for business" as one magic assistant that can handle anything you throw at it. That picture is wrong in a useful way. LLMs in…
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You have probably already leaned on a large language model this week without naming it. That support chat that understood your question instead of forcing…
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The demo worked. The pilot looked fine. Then the invoice arrived, the review queue grew, and the savings quietly disappeared.
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Someone says "I built an app with NLP" when they really mean "I called the ChatGPT API." Someone else asks "which LLM should I use" when they're actually…
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Most teams pick agents or workflows by vibe. The demo looked smart, the diagram looked clean, and nobody wrote down what the choice would cost per run.…
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You do not need the best model. You need the model that matches the control you actually require.
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You already agree with the screening criteria. Ambiguity, error cost, evidence needs, verification effort, simpler alternatives — you can recite them. Then…
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You keep hearing three answers to the same question: "Just prompt it better." "Add RAG." "Fine-tune the model." They get offered like interchangeable…
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You can explain RAG. You can sketch an agent loop. But when a real question lands on your desk, do you know whether to retrieve, route, or just answer?…
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Most people assume RAG is a different kind of model—a smarter cousin of the plain LLM. It's not. RAG doesn't change the model at all. It changes what goes…
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You asked a chat tool to describe a photo and got a polite apology instead. You asked an image tool to draw a picture and got something that looked…
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Most teams ask the wrong question first: "Can an LLM do this?" The better question is: "What does this task actually require?" An LLM is a skilled pattern…
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