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Intro to TLA+ for the LLM Era: Prompt Your Way to Victory

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Why This Matters

This article highlights how advanced LLMs can now generate TLA+ specifications, making formal verification more accessible to engineers and researchers. By leveraging AI, users can more easily verify system correctness, reducing errors and increasing reliability in complex software and hardware systems.

Key Takeaways

Most engineers’ first objection to using TLA+ is, the syntax is hostile. It looks like LaTeX, not like code. But now, frontier LLMs can generate TLA+ easily. It’s still your responsibility to understand your system and define what “correctness” means, and you need a high-level understanding of temporal logic. I’ll explain temporal logic in this article. At the end I’ll show an example prompt to start a TLA+ spec with Claude.

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A toy problem #

Here’s a classic puzzle. You have a can of beans. Each bean is white or black. The can starts nonempty. While there are at least 2 beans:

Choose 2 beans.

If they’re the same color: discard both, add 1 white bean.

If they’re different colors: discard both, add 1 black bean.

Two questions:

Can the number of beans ever reach zero? If the algorithm terminates with b = 1, what must have been true at the start?

You could think really hard. Or you could write it down in TLA+ and let a model checker answer both questions automatically. The whole point is to avoid thinking—or at least, to have a machine verify that your thinking was correct. Or convince your friends that your thinking is correct, or convince the peer-review panel for your research paper.

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