Transcript
A (0:00)
It kind of feels like AI slot and it's a very subtle thing that makes it feel that way. Common mistake I see with prototyping is people don't think about context within that 360 degree way. People just write a quick prompt or a quick little mini spec and expect the prototype tool to be able to create something as high fidelity as what they used to create before. So the next type of context for us to think through is the data context. JSON is a really good way to define structure structured data. And I have this data file, it's completely separate. I can just replace it with psychedelic rock, save it. And now our prototype is going to use a completely different data set. We've moved from a really fast assembly line approach to much more of a jazz band.
B (0:42)
Okay, welcome everyone. My guest today is Ravi. Ravi was previously the CPO of Tinder, but now he is a product advisor and full time vive coder.
A (0:52)
I do do a lot of vive coding. Yes, you do. Right?
B (0:54)
Yeah. And I think, Ravi, you've done more thinking on AI prototyp thing that anyone else I know. So, yeah, really excited to have you demo your workflows and show us how it should be done.
A (1:04)
Awesome. I'm excited for the conversation. Yeah.
B (1:06)
So before we get into the demo, maybe let's talk about, you know, this is like buzzword going around like you shouldn't do prompt engineering, you should do context engineering. But what does that actually even even mean? Like, do you have some slides talking about that?
A (1:17)
Yeah, I've got some slides. I've talked to a few companies about this. I actually really like the shift away from prompt engineering to context engineering because I feel like it does a much better job framing what is actually the task at hand. I have a few slides that I pulled together which think about what is context engineering from very first principles of what are LLMs doing and why is context so important? The move to context engineering, I think captures this overall shift from the idea that you're having a conversation to the idea that you're actually designing all of the information that an LLM or other AI models need to do their work. So I like this definition of context engineering, which is context engineering is designing and building systems that provide an AI model with the right information and tools to accomplish the task. And I think a lot of the. A common mistake I see with prototyping is people don't think about context within that 360 degree way. And as a result people just write a quick prompt or a quick little mini spec and Expect the prototype tool to be able to create something as high fidelity as what they used to create before when they had all of these different artifacts that are a critical part of the product lifecycle. And so something that we'll walk through today is how you can think about context in a more full stack way and pull in the essential elements that include the thing that looks a lot like a prompt or a spec, as well as other types of context that can be really helpful in terms of getting the output that you want from an AI prototyping tool or a vibe coding tool.
