Drawing the Line: What Deserves an LLM and What Doesn't

The three questions I use to decide what stays deterministic

Part 2 of an ongoing experiment: building an LLM-powered support agent with deterministic boundaries. The companion repo grows with the series.

Every AI feature reaches the same fork. This piece here — does it get a model, or does it get a method?

Go wrong one way and you've built a rules engine that can't read a sentence. Go wrong the other and a language model is deciding whether someone gets their money back.

"The AI interprets intent, software enforces policy" is easy to say — it's the principle this series started from. Applying it to a specific component on a specific Tuesday is the hard part.

Drawing the Line: What Deserves an LLM and What Doesn't

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How LLMs Actually Work: The Explanation Nobody Else Gives You

I don't trust the LLM to classify my email. So I don't let it.

Let your LLM take real-world actions — without giving it the last word