State-Of-The-Art Prompting For AI Agents

Meta prompting is turning out to be a very, very powerful tool that everyone's using now. It kind of actually feels like coding in, you know, 1995. Like, the tools are not all the way there. We're, you know, in this new frontier. But personally, it also kind of feels like learning how to manage a person (laughs) where it's like, how do I actually communicate, uh, you know, the things they need to know in order to make a good decision?

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Welcome back to another episode of The Light Cone. Today, we're pulling back the curtain on what is actually happening inside the best AI startups when it comes to prompt engineering. We surveyed more than a dozen companies and got their take right from the frontier of building this stuff, the practical tips. Jared, why don't we start with an example from one of your best AI startups.

I managed to get an example from a company called ParaHelp. ParaHelp does AI customer support. There are a bunch of companies who- who are doing this, but ParaHelp is doing it really, really well. They're actually powering the customer support for Perplexity and Replit and Bolt and a bunch of other, like, top AI companies now. So, if you- if you go and you, like, email a customer support ticket into Perplexity, what's actually responding is, like, their AI agent. The cool thing is that the ParaHelp guys very graciously agreed to show us the actual prompt that is powering this agent, um, and to put it on screen on YouTube for the entire world to see. Um, it's, like, relatively hard to get these prompts for vertical AI agents 'cause they're kind of like the crown jewels of the IP of these companies, and so very grateful to the ParaHelp guys for agreeing to basically, like, open source this prompt.

Diana, can you walk us through this very detailed prompt? It's super interesting, and it's very rare to get a chance to see this in action.

So, the interesting thing about this prompt is actually, first, it's really long, it's very detailed. In this document, you can see it's like six pages long, just scrolling through it. The big thing that a lot of the best prompts start with is this concept of, uh, setting up the role of the LLM. You're a manager of a customer service agent, and it breaks it down into bullet points what it needs to do. Then the big thing is telling the- the task, which is to approve or reject a tool call, because it's orchestrating agent calls from all these other ones. And then it gives it a bit of the high level plan. It breaks it down step by step. You see steps one, two, three, four, five, and then it gives some of the important things to keep in mind that it should not kind of go weird into calling different kinds of tools. It tells them how to structure the output, because a lot of things with agents is you need them to integrate with other agents. It's almost like gluing the API call. So, the- it's important to specify that it's gonna give certain, uh, output of accepting or rejecting and in this format. Then this is sort of the high level section, and one thing that the best prompts do, they break it down sort of in this markdown type of style, uh, formatting. So, you have sort of the heading here, and then later on, it goes into more details on how to do the planning, and you see this is like a sub-bullet part of it. And as part of the plan, there's actually three big sections, is how to plan, and then how to create each of the steps in the plan, and then the high level example of the plan. One big thing about the best prompts is they outline how to reason about the task, and then a big thing is giving it- giving it an example, "And this is what it does." And one thing that's interesting about this, it- it looks more like programming than writing English, because it has this, uh, XML tag kind of format to specify sort of the plan. We found that it makes it a lot easier for LLMs to follow, because a lot of LLMs were post-trained in RLHF with kind of XML type of input, and it turns out to produce better results.