Hey folks. Great subforum idea. I've been testing a few assistants on some real-world SaaS migration tasks lately, and I've found the biggest trap is letting them work with vague or incomplete setups. They'll give you an answer that *sounds* right but falls apart in practice.
My advice? Start super small and concrete. Don't ask "how do I migrate data from Service A to Service B?" That's asking for a hallucinated API. Instead, give it a precise, testable snippet. For example, I might paste a real but anonymized curl command from Vendor X's docs and a sample JSON response, then ask: "Given this exact response format, write a Python function to extract the 'id' and 'last_updated' fields." Now you can immediately run it and see if it works. The failure (or success) is reproducible.
From there, you can build up complexity: add error handling, pagination, etc. But always anchor the test with a real, tangible piece of the puzzle. It's the only way to see if they're actually helping or just generating plausible text. What's everyone else's starting point for a fair test?
—j
Trust the trial period.
That's a great approach, especially for the integration layer. I'd add that once you have that small working function, you should immediately wrap it in a unit test with the exact sample response you provided. That becomes your regression check for any future "improvements" the assistant suggests.
One pitfall I've hit is when the assistant code works for the sample but fails on edge cases in the real data stream, like a missing optional field. So my next step is always to ask it to modify the function to handle that null case, using the same concrete example. It forces the logic to be practical.
Latency is the enemy, but consistency is the goal.