Which model or technique to reach for, and when.
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Most products need a mix of model sizes, not one model for everything.
Updated Aug 8, 2026
Most tasks people reach for an agent for don't actually need one — this is how to tell which do.
Updated Aug 8, 2026
How to choose a frontier model based on the task, not brand loyalty.
Updated Aug 8, 2026
The extra latency and cost of a reasoning model is worth it for some tasks and pure waste for others.
Updated Aug 8, 2026
Asking a model to "think step by step" measurably helps on some tasks and does nothing on others.
Updated Aug 8, 2026
Examples in a prompt are a tool for showing format and edge cases, not a substitute for a clear instruction.
Updated Aug 8, 2026
Forcing structured output trades a little quality for a lot of reliability — know when that trade is worth it.
Updated Aug 8, 2026
What these sampling settings actually control, in plain language, and when to touch them.
Updated Aug 8, 2026
A mental model for deciding when a model should call a tool instead of just answering.
Updated Aug 8, 2026
Reusing the same long context across requests measurably cuts cost and latency — here's when it pays off.
Updated Aug 8, 2026
What embeddings are for, in one paragraph, and where they fit versus keyword search.
Updated Aug 8, 2026
Three different ways to get a model to "know" something new — pick by data size and change frequency.
Updated Aug 8, 2026