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Tell it to use red/green TDD and start things off with an already configured test suite, maybe with a single test that asserts 1+1==2.

Make sure it know how to run the tests before it starts writing any additional code.

Then set it a clear goal.

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Basically all the examples of LLM's building impressive things have been because they have human written tests to base the implementation on. If you have an LLM write the tests the results are far less impressive or valuable.
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Yes and LLMs are known to cheat on tests written by them.
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Last year when they added computer use to Claude web I was excited to try it out.

I just asked it for a code snippet and it ended up setting up a whole repo in a docker container or something.

Even volunteered a test suite. This genuinely amazed me.

...until I checked the tests.

It was just console.log("Tests passed!")

AGI 2027

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It's not always cheating either. They aren't intelligent, so they don't actually understand the purpose of the tests or can build them to define the actual semantics of the problem space. It's literally just next-token prediction based on the codebase and prompt. Cheating implies that they have agency, and ironically agents don't.
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I’ve found some success is small projects, with limited scope, in a greenfield.

I’m terrified to attempt agentic anything in the repo my job actually cares about. I triggered it once by accident, when the agent was first rolled out and enabled by default… it broke everything. Now I just use ask mode, and even that is wrong half the time, and once it goes wrong it just keeps getting worse.

I saw a post from Dave Plumber who vibe coded up a new cross platform task manager. He said his spec document for the AI was 107 pages long. So maybe what I’m doing wrong is not giving the AI a literal novel of spec.

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> He said his spec document for the AI was 107 pages long.

This sounds like programming but with extra steps that make it take longer with less reliability.

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Ikr, at that point the code itself is a better way of encoding the information.
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There are two 'camps' with respect to AI.

One camp already knows that Neural Nets don't work and are a dead end.

The other camp hasn't yet figured out that Neural Nets don't work, but are convinced that they do (or eventually will), because they think everything always improves over time in a linear fashion.

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You're appealing to ambiguity. All you've said is you have failed—how is anyone supposed to know what went wrong?
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I'd be happy to screenshare with you if you like, we can work on something trivial or open source. Half an hour should be more than enough to see whether you're doing anything obviously self-sabotaging.
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What is “it” specifically and what languages are you using?
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