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8d • General discussion
What if you could actually see why your AI fails?
Been thinking about something lately and wanted to get your thoughts.
Does anyone else feel like their LLM apps are super hit-or-miss? One minute it works like magic, the next it completely face-plants on a simple task. It's kinda frustrating when you're trying to build something reliable.
I stumbled onto this idea called MCP, which is basically just a fancy way of talking about how your AI uses the "tools" you give it. Think about an AI assistant that can book flights. The MCP is the set of steps it takes: finding the right airline website, putting in the dates correctly, selecting the seats, etc.
The problem is, most of us are just kind of guessing if it's working right. We run a few manual tests, it looks okay, and we push it out into the world, just hoping for the best.
But what if you could actually get a score for how well your AI performs those steps? Like a report card that tells you, "Hey, your AI is great at finding dates, but it messes up the arguments for the seat selection tool 50% of the time."
That's where this DeepEval thing comes in. It lets you test that stuff automatically.
Being able to actually measure this means you can pinpoint exactly where your app is weak and fix it. Your app stops being a gamble and starts being dependable.
And honestly, in a world where everyone is launching an AI app, the one that actually works consistently is the one that's going to win. People will trust it more, use it more, and recommend it. That's how you get a real competitive edge and make your project profitable.
Anyway, I was reading their getting started guide and it got my wheels turning. The link is here if you wanna check it out: https://deepeval.com/docs/getting-started-mcp
I'm curious, how are you all handling this right now? Are you just testing things by hand or do you have a system for it?
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What if you could actually see why your AI fails?
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