0:02 Creating a prompt with an LLM. It is 0:05 better to be long-winded to make the 0:07 point sufficiently clear to the agent. 0:08 Here's an example. When will it rain 0:11 versus will it be raining this upcoming 0:13 weekend in Nearest Fall? See the 0:15 difference. Specificity, on the other 0:18 hand, is more about requesting context. 0:21 Tell the agent as much information as it 0:23 needs to know in order to answer that 0:25 question. Here's an example of that. 0:27 Generate 10 ideas for an AI campaign 0:30 versus generate a list of 10 ideas for 0:33 an AI campaign. Pitch each idea with a 0:35 slogan, target audience, and a timeline 0:38 of execution. All of these ideas should 0:40 relate to agents. Here are a few other 0:43 guidelines that can improve your 0:44 prompting skills. First thing that 0:46 you're going to do is assign a role to 0:47 the model. For that reason, whichever 0:49 one you want, Chad or Claude, it needs 0:52 to understand who they are in this 0:54 context. So give them a persona of a 0:56 technical writer that knows how to build 0:58 agents or software engineer. It's 1:00 important that the model has a persona 1:02 so that way the responses, engagement, 1:04 interactions can all be contextually 1:05 appropriate. In this case, I'm going to 1:08 just write you're a technical writer 1:09 with the knowledge on how to build 1:11 agents. Right? The second one is going 1:13 to be about clearly stating the 1:14 requirements. So take the earlier part 1:16 of the video where I'm basically 1:18 breaking down what it needs to have in 1:20 order to write a good system 1:22 instructions. And that means like you 1:24 know the error handling, the personas, 1:26 the sub goals, the step by step and just 1:30 put that as the second part. Remember 1:32 this is a collaboration. So if you know 1:34 how to make a good one, they need to 1:36 know how to make a good, right? Okay, 1:38 here you go. Um, 1:41 now the third one is going to be 1:43 providing an example. The great thing 1:45 about this is that if you go into your 1:47 retool space, I'm going to go to mine 1:49 and then you click into agents tab in 1:52 the top right corner, it's going to say 1:54 plus agent. You click in there, there's 1:56 a bunch of examples that you can use. So 1:58 they're templates that are of successful 2:00 working agents. In my case, I'm going to 2:02 take the meeting prep. I'm going to 2:04 create 2:06 and I'm just literally going to copy 2:08 these instructions. You can use either 2:09 one of these, right? Um, and I'm going 2:12 to go in here and say here is an example 2:17 of a working irritant and tool. Okay, I 2:21 pasted it in. Okay, fourth one. You got 2:24 to format and break down the task. So, 2:27 the same way that we're able to read 2:29 with spaces and not all have it all 2:31 together, it is also important to have 2:33 all these separations so the LLM has an 2:36 easier time reading all this. uh makes 2:38 it easier for you, makes it easier for 2:40 them, and as less prone to errors 2:42 because you have to go back and like 2:43 actually check your work and see where 2:46 some mishap might have happened. That 2:47 way, it makes it easier for you to keep 2:49 improving the next prompts. It's not 2:51 just going to be perfect from the get- 2:52 go. The fifth one, set the tone and tell 2:56 this audience. So, this is really 2:58 important because you got to tell it 3:00 like who is this being delivered to, 3:02 right? Is it being delivered to another 3:03 agent? Then write it specifically for 3:05 that agent. If it's not, then do you 3:08 need it to be like developer friendly? 3:09 Do you need it to be like more data 3:11 science friendly? Well, like who is your 3:13 final audience that the agent is going 3:16 to convey all this voice from? So, what 3:18 I'm going to do in this case is I'm 3:20 going to now state what the agent must 3:22 be able to do. So, I've written it out. 3:25 I want an agent that connects to a sauna 3:27 and pulls data across multiple projects 3:29 every week and analyzes task and 3:32 analyzes due dates the activities but to 3:35 flag items as completed in progress or 3:37 at risk. Right? And there's a bunch more 3:39 instructions about like telling you like 3:41 hey this is built for retail agents and 3:44 the output is going to be for the agents 3:46 to read and to execute. 3:48 Now the next one that I would recommend 3:50 is to include affirmations. So say do 3:52 this or don't do that. Right? Um it 3:55 gives it more clear direction and a more 3:58 desired output with that same one. You 4:00 might have seen it already is the 4:02 inclusion of just even just one sentence 4:04 that tells your model to ask questions. 4:07 Just as simple as that. So I've written 4:09 as whenever unsure how to proceed or if 4:11 you want more clarification, ask 4:13 questions before continuing with your 4:15 reasoning. This would actually be a 4:16 great time to just put a spacing since 4:18 it's almost like a uh like an asterisk 4:21 to the whole interaction. Go ahead and 4:23 click it. Send it over. see what system 4:25 prompt am I create and then here are 4:28 some quick clarifications that I need to 4:30 ask. That's why it's important to have 4:31 that part of the question. Um I can 4:34 answer all these and then we can 4:36 proceed. 4:43 Okay, amazing. We're just now getting 4:45 results about like how to actually write 4:47 and you can see it's actually kind of 4:48 similar to the example that was 4:50 previously provided even the way that it 4:52 is formatted and everything which is 4:54 super cool. Um and all this you can take 4:58 now and then put it into your agent 5:00 itself and then start building the 5:01 tools. Uh if you see anything that you 5:04 want to improve, this is where you would 5:07 correct and change specific parts of the 5:09 output. So this is number eight and 5:11 again it's a collaborative process. So 5:13 you need it to tell you like how it 5:16 thinks that the instructions should be 5:17 written. You should read through them, 5:19 be thorough with them and then give be 5:21 like that part was great like processing 5:23 flow. This is really great. I like that. 5:25 Uh I think this part of the available 5:28 tools can just be as simple as like a 5:30 name of tool and then also brief 5:32 description. So I have to go back and 5:33 forth, right? And the last one is test 5:35 different LLM and props. It is really 5:38 important to not only just rely on one 5:41 single model, but try out different 5:42 models because each of them has their 5:44 strength and their weaknesses. So, see 5:46 how each of them are either more 5:48 creative or more practical and see which 5:50 one fits your style best. Um, I'll 5:53 invite you to go check out these 5:54 templates since you already have created 5:56 that. Um, and then the cool thing is 5:58 that you can chat with them. There's 6:00 many ones like for example, there's an 6:01 automated chargeback fighter. From 6:03 there, you can see like the tools, you 6:05 can see how it was made. And I would 6:07 just say like go deploy, go make and 6:09 have fun with this, you know. If you 6:10 have any questions, comment down below 6:12 and I'll see you next time. 6:16 [Music] 6:17 [Applause]
