The biggest model isn't always the best choice. For many real tasks, a small model is cheaper, faster, private and good enough.
"It looked good when I tried three examples" is not an evaluation. A practical guide to building a small, honest test set for your AI feature.
What a token is, why long conversations go weird, and why the model that remembered your project details an hour ago now calls you "the user."
Agents that browse, code and click on your behalf are powerful. Here's how to give them a job without giving them the keys to the building.
Retrieval-augmented generation, without the jargon: let the model look things up in your documents before it answers.
Why language models make things up, why they sound so sure about it, and practical ways to catch fake facts before they reach your report or your thesis.
Context, examples, constraints and a clear output format. The same things that make a good brief for a human make a good prompt for a model.
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