ChatGPT agent mode: The “little helper” that transformed recruiting, crafted user personas, and solved parking nightmares | Michal Peled (Honeybook)

Summary

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How I AI — Episode Summary

Episode Overview

Title: ChatGPT Agent Mode: The “Little Helper” That Transformed Recruiting, Crafted User Personas, and Solved Parking Nightmares
Host: Claire Vo
Guest: Michal Peled, Technical Operations Engineer at HoneyBook
Release Date: December 8, 2025

In this hands-on episode of How I AI, Claire Vo and guest Michal Peled explore how Michal uses ChatGPT’s agent mode to automate tedious business tasks. From revolutionizing HoneyBook’s recruiting process and turning user personas into chatbots, to solving the perennial San Francisco parking woes, Michal demonstrates practical, replicable AI workflows for everyday work and life. Expect transparent advice on prompt engineering, thoughtful discussion on user experience, and inspiration for making AI your own “little helper.”

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Key Discussion Points & Insights

1. ChatGPT Agent Mode in Recruiting

[02:18–23:20]

• Problem:
  Recruiting teams at HoneyBook were losing countless hours manually searching LinkedIn for candidate matches against specific job descriptions.

• Solution:
  Michal used ChatGPT’s agent mode as an interactive “little helper” to:

  • Log into LinkedIn
  • Search for profiles aligned with a given job description
  • Apply team-specific filtering criteria (e.g., candidates in Israel, employed at Israeli companies, actively using LinkedIn, tenure requirements)
  • Present results in a scored table

• Prompt Engineering Approach:

  1. Define the agent’s role: "You’re an IT recruiter."
  2. Describe the task in detail, including login steps and candidate criteria.
  3. List restrictions/instructions matching the exact recruiter workflow—gather these via interviews with the hiring team.
  4. Specify how many results are needed and request a “match score” for prioritization.
  5. Allow for human handoff: Build instructions for when the agent should pause or ask for user input.

• Results & Team Reaction

  • The agent generated five candidate matches—in just 10 minutes.
  • Four of five candidates had not yet been found by the team; one was already in-process.
  • Michal: “Now it’s going to be a real part of their hiring process, freeing their time to do other things that they love and appreciate a lot more.” (21:57)
  • Improved not only speed, but quality—surfacing edge-case candidates.

Memorable Quotes:

• Claire: “If you can codify what a person’s step-by-step workflow is and put it in a simple prompt... you can replicate and automate that at scale.” (08:11)
• Michal: “The first thing they said was: out of these five, four of them were never found by us manually and they really fit the description.” (20:07)

Notable Tip:
“Think of agent mode as a little helper. That framing will help you come up with good prompts.” — Michal (10:26)

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2. Creating Interactive Persona Chatbots from Research

[23:50–41:19]

• Problem:
  In-depth user research was trapped in documents, making it inaccessible for decision-making.

• Solution/Process:

  • Turned five research-backed Personas into distinct ChatGPTs that embody each user type.
  • Instead of just uploading files, Michal distilled behavioral, emotional, and practical traits into prompt instructions. The chatbot “became” the Persona, rather than just referring to summary notes.
  • Used Google’s NotebookLM to extract and draft precise “identity” prompts, leveraging source verification and citation features.

• Prompt Crafting:

  • Defined attributes: core identity, mindset, decision-making style, business needs, technology stack, social media preferences, etc.
  • Added strict guardrails: “Don’t make up text that is not in the research” to avoid hallucination.
  • Capped prompts to fit ChatGPT custom GPT character limitations; further refined for clarity and safety (e.g., avoiding offensive language or inappropriate content).

• Results:

  • Now internal teams can “talk to” a Persona chatbot for ideas, ad feedback, UI testing, etc.—effectively simulating real target users on demand.
  • Each Persona is used dozens of times for brainstorming and product planning.

Memorable Quotes:

• Michal: “I realized I’m not going to rely on uploaded files... The instructions here will be: you are that person, and this is your belief system.” (27:40)
• Claire: “So many people go to just plain ChatGPT, like ‘Give me five headlines for an ad campaign,’ as opposed to going and sitting with your fake Persona and saying ‘What ad campaigns would work on you?’” (40:09)
• Michal: “Personalizing those Personas has changed the way we work with them.” (40:09)

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3. Solving the San Francisco Parking Headache with AI Calendar Automation

[41:19–48:15]

• Problem:
  HoneyBook’s office is near Oracle Park; on San Francisco Giants game days, parking prices spike unpredictably, catching employees off guard.

• Solution:

  • Used ChatGPT to scrape the baseball schedule, filter for daytime home games (the only ones affecting morning parking), and generate a custom .ICS calendar file with “free” all-day events.
  • Distributed the calendar team-wide, so everyone could plan commutes accordingly.
  • Added a text-based list of all included games for transparency and verification.

• Workflow Takeaway:
  Demonstrated how even “dull” admin tasks can be streamlined with AI, saving time and money for teams.

Memorable Quotes:

• Michal: “ChatGPT doesn’t care [if I double-check its work]. So it thought for 36 seconds, provided me with a file and also with a list of all the games.” (45:50)
• Claire: “You probably could have found an SF Giants schedule calendar. That’s not exactly what you wanted... Now you have this filtered to what you want.” (47:12)

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Notable Quotes & Moments

• “Thinking about it as a little helper will really help you come up with good prompts for it.”
  — Michal Peled (10:26)

• “As you watch this, what I hope our listeners and viewers are taking away is you don’t just have to rely on text prompts and chats when you’re using these tools... The next evolution of these LLMs…you actually have a lot more tools.”
  — Claire Vo (12:22)

• “Four of them were never found by us manually... I want to approach and try to get them for an interview. And the fifth was one we caught manually who’s already coming in. To me, it was a great sign of quality.”
  — Michal Peled (20:07)

• “This is your moment, internal tools teams. You can have really high impact and do great work... It’s moving faster than you can even get some of these experiences into product.”
  — Claire Vo (51:17)

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Pro Tips on AI Prompting (Lightning Round)

[52:35–55:52]

• When facing unhelpful or hallucinated AI output:

  • Copy your prompt into ChatGPT and explicitly tell it what’s wrong: “Output is inconsistent, contains too many hallucinations, invents things…”
  • Outline exactly what you expect, then give ChatGPT explicit permission to add, remove, or rewrite as needed.
  • Michal: “Giving permission to change, delete, remove whatever provides a better output... [Otherwise,] ChatGPT tends to be a pleaser.” (54:32)

• All-caps for emphasis?

  • Michal half-jokes about using all-caps in prompts for emphasis: “No one can persuade me otherwise.” (53:01)
  • Ultimately, a structured, explanatory approach leads to much better results when revising prompts.

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Timestamps for Key Segments

• Agent mode overview & recruiting use case: 02:18–23:20
• Persona GPT creation: 23:50–41:19
• Parking calendar automation: 41:19–48:15
• Discussion on Michal’s role & the value of internal tools teams: 49:16–52:35
• AI prompt improvement techniques: 52:35–55:52

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Episode Takeaways

• AI Agents are most powerful when you take the time to precisely model the real process you want to supercharge.
• Interview teammates, codify steps and criteria, and treat LLMs as collaborators, not just passive responders.
• Custom AI personas unlock user empathy at scale in product and marketing.
• Even small personal pain points—like parking—can become opportunities for automation.
• Prompt engineering is recursive: use AI to improve your own prompts, and always provide permission and context for maximum effect.

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Connect with Michal Peled:

• LinkedIn: linkedin.com/in/michal.peled
• Company: HoneyBook

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For more episodes:

• howiaipod.com

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Transcript

[Claire Veau]

We're going to start with something that we haven't actually seen on how I AI yet, which is agent mode in ChatGPT.

[Michal Peled]

My use case was with our hiring team. Part of their workflow is to Browse through many LinkedIn profile and search for relevant candidates. It takes a lot of time.

[Claire Veau]

Let's talk about the prompt. I'd love for you to go through how you thought about structuring it to make it effective with the agent.

[Michal Peled]

I want a little helper. I'm a recruiter. I want someone who is like me. So I started by telling you you're an IT recruiter and then I described what I wanted to do.

[Claire Veau]

I love that you called it your little helper, because don't we all want an AI little helper?

Welcome back to How I AI. I'm Claire Veau, product leader and AI Obsessive, here on a mission to help you build better with these new tools. Today I have Michal Peled from Honeybook, their technical operations engineer, who's building tons of internal tools and automations to make their team's life easier and reduce friction. Michal is going to show us some advanced features of ChatGPT, including agent mode, replicate not one, but five of their Personas as AI identities, and save me a lot of time on my commute using ChatGPT. I'm really excited about this episode. Let's get to it. This episode is brought to you by Brex. If you're listening to this show, you already know AI is changing how we work in real practical ways. Brex is bringing that same power to finance. Brex is the intelligent finance platform built for founders with autonomous agents running in the background. Your finance stack basically runs itself. Cards are issues, expenses are filed and fraud is stopped in real time without you having to think about it. Add Brex's banking solution with a high yield treasury account and you've got a system that helps you spend smarter, move faster and scale with confidence. One in three startups in the US already runs on Brex. You can too, @brex.com HowIAI Michal, thank you so much for joining How IAI. I'm excited to see what you have to share.

[Michal Peled]

Thank you so much for having me.

[Claire Veau]

We're going to start with something that we haven't actually seen on how I AI yet, which is agent mode and in ChatGPT. And so I'm wondering if you can just go ahead and dive into what was the problem that you were trying to solve and why was this Agent mode, this agentic Browsing the solution to.

[Michal Peled]

The problem you're having, Our problem was, you know, same as our customers are having. You have to do your job. You have a job that you really love doing and you have your proficiencies and expertise. However, you spend a lot of your time doing the mundane, thoughtless, manual repeating work in order to do to get the information that you need. So my use case was with our hiring team and as a recruiter, when you get a job description that you need to recruit to find candidates for part of their workflow is to Browse through many LinkedIn profile and search for relevant candidates that may be relevant for the job descriptions. And it takes a lot of time, can be hours of browsing through profiles and going through all of that characteristics that they're looking for. So I wanted to take that load off of them. And ChatGPT agent mode came just in time. We all talk about what agent is and what agents do and how we can use them in ChatGPT. It's very simple to understand. So you just open a chat with ChatGPT, but then you add an instruction and turn it into an agent mode very simply from the toolbar. And once it goes into agent mode, it means that it can take the prompt or you can actually use specific prompts to tell it not just to search for information online, but also to perform actions for you. And why did I need it in this case? Because I needed to log into LinkedIn. I don't want it to just search for profiles on LinkedIn, just, just profiles that are publicly accessible. That's not the information that I need. So I needed to log in into LinkedIn and I needed it to perform search and I needed it to go through the profiles and, and look for the restrictions that I want to give it. And those restrictions were provided by the actual hiring team that they actually use it as requirements for potential candidates that they find.

[Claire Veau]

Yeah, let's talk about the, the prompt really quickly, because I think this prompt is, is interesting as I'm reading it, and I'd love for you to go through how you thought about structuring it to make it effective with the agent, of course.

[Michal Peled]

So I usually start my prompts, begin my prompts with telling the GPT its role. And so here I told it, you are an IT recruiter. I want a little helper, right? I'm a recruiter. I want someone who is like me that will assist me with my job. So I started by telling it, you're an IT recruiter. And then I described what I wanted to do what? The task is log into LinkedIn using my account. If not already logged in, let me take control and log in. It is something that is possible find up to five LinkedIn profiles where the current title and job description match the attached job description. And here is the part where I just uploaded a job description. In this case, it's for an engineering role. Okay.

So I have the job description and I have the.

You are an IT recruiter. This is your job. This is the task. And I provide like a full description of the task, actually, actually describing what an actual IT recruiter would do. And then I added restrictions or special instructions. It doesn't matter how you call them, but these are important because these give. Don't just search for something that matches the description, do it the way that we do it. And when our hiring team goes into a search, they have specific criteria that they go for. So I collected these and I added it as a list, as a restriction. I could call it instructions. It would, would have been the same. So candidates must be from Israel because the job is being filled up in Israel or currently working at an Israeli company. And they must be active in LinkedIn within the last three months because that's something that our iron team is looking for. And the current job job role must be close enough to the open role and entitle and seniority. And also something that is special. The candidates must either work in their current workspace more than a year or they can be unemployed, but no more than a year and have worked in their last workplace for over a year. These are all things that I, I didn't invent them. They were taken specifically from our hiring process.

[Claire Veau]

What I love about this is exactly what you said, which is first, I love that you called it your little helper, because don't we all want an AI Little Helper? That is, that is my goal. Maybe I'll rebrand my product to just Little Helper. But what I like about this is, you know it. When you're building a tool like this or a prompt like this, the simplest way to get to a good outcome is simply interview somebody and say step by step, just tell me what you do. Like tell me what you do. And if you can codify what a person's step by step workflow is and you can put that into a pretty simple prompt, which here it's only a paragraph and you know, three or four bullet points. You can replicate and automate that at scale. And typically this is not the highest order thinking you want your recruiter or sourcer to do. You don't want them just to build a list and be looking is this person here a year or not? That is an input to what you hope is a great recruiting process, great outreach, all that kind of stuff. So one, I think it's just really great to interview your colleagues and say, how do you do your job and what parts do you hate and let me automate them. The second piece that I think is really interesting here is you're actually pretty specific about a couple outcomes. You're specific about the number of candidates that you want. So I think that's really helpful. You're specific about a kind of threshold of matching your criteria. So you say 70%. And I often find these LLMs are very, you know, 1 to 70% matching. Like it's not, it's not pure science, but they're pretty good at. At following a general threshold. So I think that is really interesting. And then the last piece I want to call out, which we'll maybe see in the demo, is agent, while it can be agentic and independent, can also be a copilot and collaborator. And so you actually instruct the agent when you're going to take over and when they're going to take over. And so I think that is also really interesting things for folks to know is you don't just have to like, press the button and walk away and let the agent run. You can press the button and say, hey, wait, when you get to this point, let me take the next step and then you can go on from there. So that's really interesting.

[Michal Peled]

Exactly. Or if you encounter a problem with this and that, stop and ask for my assistant. And that's exactly the agent. That's exactly the agent mode. Thinking about it as a little helper will really help you come up with good prompts for it.

[Claire Veau]

Okay, I think you gave us our. Our show title will be ChatGPT Agent Mode. Your Little helper. So let's see how it runs.

[Michal Peled]

Exactly, exactly. So once you start running it and it's. This is something that is mind blowing for anyone who tries it for the first time, even the ones who are very proficient with using AI tools. Suddenly you get a computer, open it up in your.

You see your little helper actually doing things in the computer. So it started by reading my job description. And then you can see it goes. It will try to go to LinkedIn. It will probably be already logged in because I logged in beforehand. And the thing I like the most, you see it's logged in and then you can See like the arrow goes and clicks on things and searches on things and go through the list. And the thing I like the most is that during all of this time you can see the thoughts of the agent. Now I will go to the feed page loaded again. I plan to click on first. I need to make sure you can see inside the brain of your agent while it is thinking.

And all of this is live. I will let it run here but we can go and see results.

[Claire Veau]

Yeah, I want to call out a couple of things because I know that often on how I AI, we have highly technical use cases for highly technical folks, but we also have a lot of people that are actually quite new to using generative AI tools and have probably been pretty familiar with ChatGPT and the direct experience. But I know if I showed my mom this or even some of my friends that maybe don't work in tech and said, hey, did you know that ChatGPT can open up a magic computer and navigate it and narrate its thoughts and look for things for you, they'd be pretty, pretty surprised. And I think, you know, as you watch this, what I hope our listeners and viewers are taking away is you don't just have to rely on text prompts and chats when you're using these tools. Now that the next kind of like evolution of these LLMs, especially the more like consumer focused general purpose ones like ChatGPT have evolved, you actually have a lot more tools. And so I just want to call out. So for some of those folks out there, I'm, you know, I'm thinking a little bit of, of my parents and some people in an older generation who are like, how do I get from here to here? I need help, you know, searching for flights or I need to do a certain kind of research on a niche site. Having sort of this expert computer operator on hand, I actually think is going to make information more accessible to folks, but it's also going to make UX and websites more accessible to folks that don't have the time to figure out how do I use the best filters on LinkedIn or those sorts of things. And so I just, I want to make sure that people that have not experienced agent mode and I know we're all on the edge so maybe all of us have just, you know, take a minute to appreciate the kind of use cases that this opens up also. It's just fascinating to watch.

[Michal Peled]

Yeah, AI operator.

Of course, first time I tried it, I just sat and watched the thoughts of the agent while.

[Claire Veau]

It was thinking, going to Our more technical audience though. A couple of things that I want to call out is one, props to the OpenAI ChatGPT team. What a great user experience design here. Like, it could feel very strange to watch an agent browse a website. It could either be boring or weird. And I think this user experience of like being able to see where the cursor is showing the reasoning and thoughts, watching it navigate is actually pretty entertaining. That's a hard thing to pull off for a, a consumer product. And so for anybody designing AI products, it's worth thinking about some of these interaction patterns here. And then again I just think about how long this would take someone to do. We're watching it because I'm trying to narrate some of the features. But you know, you could, you could walk away, you could go to a meeting while this happens or you could do something, something else.

[Michal Peled]

If you have ChatGPT on your phone, you will get notification and notification when it's done to tell you that it's done. And, and here are the results for you. We said little helper and the last.

[Claire Veau]

Thing I'll call out. I love, I really do love this use case is I do this specific thing all the time as a hiring manager, executive leader in an organization. I was constantly looking for like who's a Senior Director of DevOps and Platform Infra who is either in San Francisco or works for a San Francisco based company who has experience in dev tools that, you know, one, one change I would maybe make on your prompt is like, is one or two connections away so I can actually message them or get a backchannel reference on that. I did this all the time. I had my hiring managers do this all the time. So even if you're not in recruiting and you're just somebody who does hiring, I think this specific workflow is really, really useful.

[Michal Peled]

Yeah, definitely different excellent use case that you, you just mentioned.

[Claire Veau]

So let's actually.

Let'S look at the output. We'll, we'll let this run. But I know you have an example output for us.

[Michal Peled]

Yeah, yeah, it runs. I have an output that actually worked for 10 minutes. Just that. And within these 10 minutes I got a list of five candidates as I requested for. And you can see the match score. Well, having a match score or rank for results is something that I really love doing. It's not a must, but if you give specific, if you provide specific requirements and ask for a match score, it is easier to understand what results are more, have more quality for you. I mean otherwise you could just get A table of like these are the five results but is someone of them better or maybe a better match for what I need then the others? I won't be able to see it unless I instruct the GPT to provide me with some score. So it's not an exact science but it does give you some kind a way to compare between the results. So I will say someone who got 90% match is probably as like probably will be a better match than the 78% match and I will have to go deeper and understand why.

[Claire Veau]

Yeah. One, I'll call out a couple things here that I think are for interesting for people to look like. I know we were talking before the show, you actually made this anonymized data just so we weren't showing people's profiles or you know, showing how person A versus person B fit a specific job you're hiring for. But I will say anonymizing candidate profiles is actually a pretty standard practice in a lot of recruiters recruiting flows just to make sure you're not biased. This school, that school, this person, that person, this name, that name. And so I actually kind of like this flow where you're really just comparing the qualifications against your stated objections or objectives. And so I think that is a really interesting kind of meta flow that you're showing here. The second thing that I wanted to say as I was reflecting back on agent mode is it's almost exactly like a recruiter or Sorcerer would navigate LinkedIn except for one thing. When I log into LinkedIn I don't go straight to the job to be done. I don't go straight to that search bar and search for like VPs of engineering. No, no. I get distracted by the violent notification. I start reading the feed, I'm responding to comments, I go through my inbox and so I think like why is it 10 minutes? It's 10 minutes because it's like pretty hyper hyper focused and efficient. But it's also 10 minutes because you're not getting so distracted with all the other things in in the application and you can really just get the agent to focus on the task task at hand. So maybe it's a way for us to all break. Sorry, LinkedIn, LinkedIn growth, PMs, I apologize. But a good way for us to still get the value of these platforms without getting our time sucked into the less value generating aspects of them maybe.

[Michal Peled]

Yeah, that is correct.

[Claire Veau]

So tell me a little bit about how this was received by the team. I'd love to know the kind of outcomes here.

[Michal Peled]

Yeah, well.

I will be real and say that.

The first result that I got I was very skeptic about. So I just took the table and I sent it to our hiring manager and I told her this is the job description. This is what AI found for me in LinkedIn. If you can go through the results and let me give me feedback, are there good results? Are we familiar with this candidate? Did we try to reach out to them? Or you're looking at them and say, oh no, that's a terrible fit. I don't know why, why, why this person is even in this table. And so she went over or over them, you can see that the table as link to the direct, like the, the LinkedIn profile per candidate. So she scanned those five profile and she came back to me and she said, well, you know what? Out of these five, four of them were never found by us manually and they really fit the description. So we would want to approach and, and you know, try, try to get them to, to come for an interview. And the fifth one was actually one that we caught manually and is already coming for an interview. And so to me it was a great sign for quality. I mean it's not just a list of names. Those are actual real quality candidates that we can work with. And so now, now they want, they want the agent to run on a lot more job description. Many more job descriptions that we have provide them with more than five candidates. I wanted just five to see if, if it, if it's worth something.

Yeah, but now it's going to be a real part of, of their hiring process, freeing their, their time to do other things that they love and appreciate a lot more. Yeah.

[Claire Veau]

And I can't emphasize what you're saying enough because so many people push back on AI saying yeah, you may get speed and you may get efficiency, but you're not getting quality. And my experience has, has been the opposite of that. You get speed and you get quality. And again it's those, it's that last mile, those edge cases, those ones that are like just a little hard to find, a little hard to research where I think AI can increase the quality of that last. But, and so it was, it's amazing to see that this worked for your recruiting has given me so many ideas. Not you know, not just the recruiting use cases, but just in general one people finding use cases. I was thinking about how you could find great candidates or customers on like X or LinkedIn. And then the other thing that I think is, is really great here is just showing we don't get a lot of GNA functions. We don't get a lot of people functions. Getting love in how I AI. I feel like all the noise is about like product design, engine support and so I just love seeing the recruiters get some love here because you're the people that bring in great talent and fun colleagues to work with. So thank you for showing this.

[Shrestha]

This podcast is supported by Google. Hey everyone, Shrestha here from Google DeepMind. The Gemini 2.5 family of models is now generally available. 2.5 Pro, our most advanced model, is great for reasoning over complex tasks. 2.5 flash finds the sweet spot between performance and price and 2.5 flashlight is ideal for low latency, high volume tasks. Start building in Google AI Studio at AI.dev.

[Claire Veau]

Let'S zip to your second use case, which I think is really we're going from finding real people to creating fake people. So I'm excited about this next workflow.

[Michal Peled]

That's an excellent description of it. So let me ask you that. Imagine you're a business owner and imagine being able to talk to thousands of your potential customers all at once and gather their insights on your planned ad campaigns, planned features, product experience, all from your phone or tablet, 24 hour a day with one click of a button, actually talk to actually talk to them. I thought it's mind blowing. And so it started with HoneyBook. Invested in comprehensive customer research with a third party provider who interviewed hundreds of our target small business owners and they created five detailed buyer Personas. But the research was trapped in documents, hundreds of pages of insights that teams rarely referenced because it was too time consuming to extract actionable information when making product or marketing decisions. So the end goal was we have five Personas. We want to talk to them. Let's create a chatgpt that is that person, that actual person. And so I started with and here that there are some technical takeaways, but here I want to put the spotlight on the thinking process because it's very easy to go to ChatGPT and everyone with a subscription tier of.

It'S not the A Plus subscription tier and above can create their own custom GPT. And so you go to create a custom ChatGPT.

It's a quite simple process. You add a name to your GPT, a description, but the most important parts are the instructions you're providing it with and the files that you can upload as a knowledge for that chat that you're talking to. So I needed five like them. But first I thought, okay, this is all I this is All I know about custom ChatGPT, I can basically take all of the documentation from the interview and just upload all of the files, text files, presentations, whatever, into this custom chatbot, provided with some instructions on how to answer what the research was about, what to say, what not to say, and ask it questions about the research itself. But that's not what I wanted. So I was like, okay, if I'm taking just the files per Persona, so I'll concentrate in one Persona, take the files related to that Persona, upload it to the chat, instruct the chat what that Persona is, how to read the files, what's included in the files, then I'll be able to maybe ask about that Persona, and I will probably get answers like, that Persona would have done this, or that Persona would prefer that. But it's not like talking to the Persona. I'm still talking about the Persona and not with it. And so I realized I'm not going to rely on uploaded files for the ChatGPT. I need the instructions to be the.

Main and most important part of what consists of that Persona. And the instructions here will not be what's in a file or talking about someone else. There will be exact instructions that goes, that go like you are that person and this is your belief system, and this is how you run your business, and this is how you deal with media, and this is how you deal with technology. And everything has to be super, super, super tight. So that chatgpt, once running live, will actually become that Persona that can answer based on what it knows about itself, not files that are attached to it. And so I needed to bridge that gap. I have all of the research there.

I need to make a person out of it, or five of them, actually.

[Claire Veau]

Okay, so you heard it here first. This is our first how I AI where we are manufacturing not one, not two, not three, not four, but five people. So show us the process.

[Michal Peled]

Yeah, so I thought about it, and then I decided to go to another tool that I really like. This is NotebookLM.

It's a Google's tool. And NotebookLM, the thing I like about it the most and the reason I picked that tool in order to construct the instructions or the prompt per Persona is actually there are several reasons, but one of them is NoteBookLM allows you to upload your own sources and can answer all only based on these sources and not things that it goes and finds online or things or knows or filling in the gap. This is the information you ask me about something I will answer based on the knowledge that you provided alone. Also, it allows you to check and uncheck the sources that you want to rely on. So I can ask a question without relying on the buyer journeys, for example, and then the answer will not include that part of the knowledge things that I cannot do in ChatGPT or anything else.

And then there's this chat part within the notebook where it's Gemini based. Gemini is Google chat model. And in this chat window I prompted the chat. Again, you are an expert prompt engineer, again with the role what you are specializing in creating custom GPTs by providing strong AI prompts. And then the mission, what your task or mission is. Your mission is to create AI prompts for custom GPTs representing entrepreneurs and small business owners where the decision makers lands on arms out. You will craft highly detailed, nuanced and authentic ChatGPT prompts for five distinct buyer Personas based on your sources. I never told it what were the Personas. It had to get it from the sources. And that's the most important part again is guidelines. I mean, prompts are nice.

They usually should come with some guidelines, instructions.

Anything that you want.

The chat to take specific care of. So in my case, the guidelines were to ensure that the prompt correctly and fully described the core identity, mindset, decision making style. I didn't want the chat to decide on itself what, what I care for about those Personas, because I knew what I care for. So I wanted their mindset and decision making style and tone and communication style. And then the business needs and the technology stack and the journey map, social media preferences. I, I pointed the chat to exactly what I needed to get out of this research. And then that's another important one. I think one should not go on without that instruction. Don't add or modify text that is not written or implied in the text. I know you're creative, I'm turning you down. The text to describe a specific Persona must remain true to the original Persona.

[Claire Veau]

Yeah, I'm laughing because yesterday I literally wrote a prompt that was like, do not make up any links. I had a thing that was making it. Do not make any up any links that are not in your source of links. Like, and it's so funny, we get so used to operating these chat bots as if they have human reasoning and sometimes they have kind of like superhuman reasoning and sometimes they just do stuff that a human would never do, like just make up something. And so I think this third prompt, we'll zoom in on it on the show, is probably applicable to a lot of things especially, especially when you're trying to constrain an LLM space to a specific set of data and inputs. So it's a good prompt. Everybody should use the don't make up stuff prompt. Your hallucination rate will drop.

[Michal Peled]

Yeah, exactly. Wow.

It is crucial to add those things and to think about them.

Um, so the result was. And another thing that NotebookLM is good at doing is you can save the responses of the chat as notes, and those notes are saved here for you to look at later. So I can show you an example of the notes that it created, but mainly you. I just took the prompts, I went over them. The important thing about the prompts is that's another strength of using NotebookLM.

It uses.

Citations. So you can actually go over.

A piece of information that the chat decided this Persona is and see where did you take it from? And, and just make sure and verify that it went through all of your data information and didn't invent anything.

[Claire Veau]

I'm gonna laugh because I'm from Austin and I'm pretty sure I know people pleaser, whatever. Parker seems like a very accurate Austin entrepreneur Persona. If folks are wondering if this is creating high quality, high quality Persona prompts.

[Michal Peled]

Yeah, yeah, yeah.

So eventually, yeah, I, I, what I did is just, I took the prompts, I did need to refine them a little. Okay. Because.

Even with all of my instructions, Gemini didn't exactly realize what kind of prompt it needs to create. So it was missing some guardrails, it was a little too long.

The custom GPT instructions are limited to 8,000 characters and it created some of the prompts being longer than that. So I did need to deal to do a little refine and create stronger prompts. So for example, and I'll show you here, in the demo, I needed to add, I used ChatGPT itself or sometimes Claude because I like working with cloud. I use them just to tighten the instructions a little and make it more robust and add guardrails. So I added, for example.

You do not act as a general person purpose assistant. You do not ask follow up questions. You avoid slang, bad language, or this distasteful content. And keep communication respectful and inspiring. You avoid political, religious, gender, or racial commentary. And I really wanted to add it. That's another key point for creating custom GPT that need to talk as a person. Because believe me, those people work with you. They are your friends. The first thing they will try with a chat GPT like Persona is to tackle them with swear words or their, their ideas about political things or. I don't know, I was going to say for. For food. So this.

[Claire Veau]

This maybe should be a default prompt wrapper on all enterprise GPTs and it would save us all a lot of. A lot of heartache.

[Michal Peled]

Yeah. So you can see it's in order of what I asked for. So core identity, mindset, business needs technology, stack, whatever. And then what you get is then it's the time to actually test them. So we created those five and I can go to Balance Blake. You can see she's one of the most talked to internal chats. So we can go to Balance Blake and I can ask her.

What kind.

Of headline.

Would catch your attention. Maybe I'll move it would catch your attention during a busy workday. Don't you why doesn't you want to know that thing about your ideal or prospect client And I can send her the question.

And then if I'm scanning quickly between meetings or juggling a few would catch my eye. Save 10 hours a week with this tool. No tech skills needed or from from chaos to clarity. One dashboard to run it all your clients don't need another email. They need this. And this Persona actually explains why even why every one of these headlines will catch their attention. And I can take the same thing and try it on. A completely different Persona like Aiden and Aidan will give me complete different answer. Aidan will say I need.

One that respects my time and speaks directly to the pain I'm feeling in that moment. Still doing admin during added days ears are to reclaim five hours a week. Other variations that might grade me win better clients without burning out and so on and so on. So each Persona actually answers based on the Persona that we got from research. And that single Persona represents thousands of potential customers. And so you can try ad lines or you can try a product journey. What would be your your best first impression when you get into a new CRM?

What would be the feature that will convince you not to churn a CRM so you can try it on them? They are 247 ready to talk to you on whatever and I really like them. Personalizing those Personas has changed the way we work with them.

[Claire Veau]

I just love this workflow. And to recap it for folks, you took a bunch of, I'm presuming pretty expensive research that probably sat in a bunch of PDFs and docs where, you know, we occasionally said head down Haz Hayden, but otherwise did not use these Personas. Use Notebook LLM to create a prompt that embodied the personality of the Persona. You put those Personas in GPTs and now you can See that dozens of times your colleagues have gone to them to brainstorm with the Persona, which I think is really interesting, and it's giving me a lot of ideas. So many people go to just plain chatgpt. It's like, give me five headlines for an ad campaign as opposed to going and sitting with, you know, sitting with your fake Persona and saying what, what, what ad campaigns would work on you. So I love this flow. We learned a little bit about the strengths of notebook LM GPTs and flipping these like sort of Personas on their head. Let's go to workflow three, which I will tell you I personally, I have a personal connection to. So people in San Francisco, listen, here's the use case for you. Let's jump to your last use case and then we can get you out of here.

[Michal Peled]

Well, this one is actually. Yeah. A really big, painful. Yeah.

Favorite to solve.

Well, imagine getting ready in the morning, driving to work, already planning ahead for your busy schedule and morning routine, only to discover that parking in your favorite parking in your favorite parking lot now costs $40 an hour instead of the usual $50 for the entire day that you paid so far. So this can ruin your entire day for sure. So the thing is, any book's office is right next to Oracle park where the San Francisco Giants play. And on game days, especially those taking place in the morning or afternoon, parking rates spike from $50 a day to 40 plus dollar per hour. Our team was constantly getting caught off guard, showing up to expensive parking or scrambling to find remote cheaper alternatives. We needed a way to know in advance when to take public public transit instead of driving to work. And so the solution was, I was thinking, okay, I think let's share a calendar, like a joint calendar, to just show you on which days parking lot prices are likely to search. I needed two things for that. I needed to figure out when gains are taking place in the ballpark. And I needed to create a calendar file I had no idea how to do. Calendar file is ICS file. This is the type. I have no idea to how to create one. Okay, whatever. Let's go to ChatGPT.

[Claire Veau]

So while you're getting this up, I am just smiling and laughing because my launchdarkly office was right behind Oracle Park. And I got there, I found a 20 a day parking. And I still have like, I texted my friend the day I had to pay like a hundred dollars.

[Michal Peled]

Yeah.

[Claire Veau]

To park. I was already down there ready for a meeting. And so San Francisco downtown is, we're coming back, people, but don't forget that the summer, the summer baseball season and sometimes they have two games a day. They have double headers.

[Michal Peled]

Yeah, yeah, that is correct. And then you have. Yeah, walk, walk over there. Just don't use your car.

[Claire Veau]

Just don't go. Just don't go.

[Michal Peled]

If you can avoid it, avoid it.

Yeah. So I was like let's try ChatGPT. I mean this should be a simple one o fully. So I tried naive one. Okay. As you can see, this prompt doesn't tell the chat you are this or that. I was like, I have a simple question. Find all home games that take place in a Rockle park in San Francisco during the next six months. I use six months because I knew it's the end of the season coming soon. So you can ask for the next year, whatever. Filter out only the games that start anywhere between morning to 2pm because if games are taking place in the evening, when we arrive in the morning, the prices are still the usual sane one. So using these dates, create an ICS file for Google Calendar. That's the calendar that we're using at work that will show these dates as an all day event. I wanted, I wanted just to see very clearly potential dates, days in my week where I rather avoid driving to the office. And a key point was availability free. Otherwise this all day event will just block my entire day, show me as busy just because the Giants are playing. Also the event description should contain the game details and time. I wanted to add that so I can verify that the game is the one that I'm thinking about, that it's actually one that is taking place there. I like to add those extra verification point, validation point just to make sure that we know what we're talking about.

And then I also added an instruction other than just output the ICS file that I need the calendar file, I want a textual list of all the dates, times and events included in the created calendar. Now basically, if it was human, they may have been a little offended by me. Why don't you Trust me? But ChatGPT doesn't care. So it thought for 36 seconds provided me with a file and also with a list of all the remaining games. Because the season is about to end. All the remaining games that are taking place in Oracle park with their dates and times and so I know all of these are included in this file. I just took the file, I installed it or added it to my personal calendar or work calendar. I also shared it with all of my team members. And then you upload it and then you can see for example, that on September 10th there's a game, Arizona Diamondbacks are, are playing the giants at first. Peach is 12:45, so better avoid driving to work that day.

[Claire Veau]

I, I love this so much because again, I have hit this problem so many times. And you don't want a calendar that has the game in the middle of your work day. Right. You want to customize. You probably could have found like an SF Giant schedule calendar. That's not exactly what you wanted. And it would have had all these games, weekend games, night games, all these things as well. Away games. Exactly. So you can have this really filtered to what you want. I'm going to give you one, one improvement that you can make to this.

[Michal Peled]

Yeah.

[Claire Veau]

Which is for the night games you should put an alert because if you park in the morning and you're still there for the night game, you should just go to the game. It's a great, great stadium.

[Michal Peled]

That's an excellent suggestion to watch the games.

[Claire Veau]

Good view. It's finally warm in San Francisco so it won't be freezing.

[Michal Peled]

Yeah. And you parked cheap.

[Claire Veau]

And you parked cheap. I've done that once or twice where I parked. I'm like I'm not leaving, honestly. I don't want to deal with the traffic. I'm just going to go, go to the game. This is a great little workflow, I think like a very good little helper personal workflow that helped you and also your team. So thanks for showing me. So just again to recap your use cases, first one we did. Oh Agents for recruiting. Loved it. So straightforward. I'm going to use that right away. Two, generated Persona GPTs and three, make your daily life a little bit easier by giving you ambient information that can help with your comm. So we're going to wrap our episode with a couple lightning round questions and then get you out of here. The first one I have to say is you are the little helper. You seem to be all over Honeybook. Just helping recruiting, helping the product team, helping the whole team. You know, tell us a little bit about your role and what you think this role will look like. Do people need a dedicated person or a dedicated team towards these automations? What do you think the future of this inside companies is going to look like?

[Michal Peled]

Okay, for sure. Well I, I love nothing more than talking about myself. So my title is technical Operations Engineer but it encapsulate a lot of other things. So I do, I research and integrate paid tools but a lot of the times you don't find the exact paid tool that you want. So I build them. I build internal tools and processes. I'm using no code solutions, automations and also coded solutions. It can be an AI powered Slack bot, it can be an internal application, it could be integrations between two different applications that don't speak with one another. So I come in the middle and I connect them.

It's not just doing things for others, it's also teaching and enabling others to do for themselves. I'm a great believer in enabling. So I do company wide presentations, I do personal advisory training classes, documentation. Actually.

As Honeybook is a platform for small businesses, okay. I see each team and department within Anibook as a small business of its own. They provide services, they collaborate with other teams, other businesses. They have their own goals, they have their own expertise, passion for different things and they all want to spend less time on manual, thoughtless, repetitive tasks and more time doing what they love. So this is where I'm coming for. This is what I'm trying to do. This is what I'm trying to provide, to take the, to take the, the, the friction away and leave you to do what you love.

[Claire Veau]

One thing I want to call out for folks is I've been in tech a long time and unfortunately, basically up until the last couple years I feel like internal tools teams were very starved for resources and occasionally starved for respect. It was like, oh, you got the product teams and their customer facing and they build all the cool products and like internal teams are always underfunded, not enough people, blah blah, blah, blah. And I think now what I love is this is the moment for internal tools teams to shine, to do legitimate, great, high impact product work. I would recommend anybody who really wants to lean into AI find their way into this kind of role because honestly a lot of times it's moving faster than you can even get some of these AI experiences into product which have a lot of like customer impact and legal implications and blah blah, blah. But if it's, if it's all internal tooling, you can kind of let it rip. And so I just want to like shout out to all the internal tools teams out there that I know today have not got the love and respect that they deserve. This is your moment. You can have really high impact and do some pretty great work and honestly do a lot to differentiate your career in, in this moment by taking advantage of the fact that you can build these tools. So I think you are a great model and I'm excited to see you do it.

[Michal Peled]

Yeah.

[Claire Veau]

Okay, last question. You're a very good prompter in fact, you create prompters to create prompts.

[Michal Peled]

When.

[Claire Veau]

AI is not replying to you the way you want, when it is frustrating, when the agent gets distracted by the notifications in its inbox. What is your prompting technique? Are you all caps? Do you yell.

[Michal Peled]

Wow? Well I, I love using all caps and no one can persuade me otherwise. I mean, there will be people saying it's just a robot, it doesn't care if it caps or not. But I'm like, no, it takes me a lot more seriously when it's all caps.

But I will say my go to technique would be to take my current prompt and then tackle the ChatGPT with my prompt, asking it to make it better. And how do I do that? It's not just this is not working make it better. Even as a person, I, I would, I have no idea what you want from me. So I just going with this is the prompt I'm using. This is, this is what's wrong with the output. Like I outline the output is inconsistent.

Contains too many hallucinations, invent things that are not there. That's the second part. And then I, it's very important for me that the prompt will 1, 2, 3, 4 I I list the things that not just what, what is wrong, but while I want it to be right. And then.

I also added I give it permission. Take away everything that doesn't work well. Yeah, you can delete things from my prompt, you can rewrite things that don't work well and you can add things that you feel will do a better job. And I feel like giving permission to change, delete, remove whatever provides a better output because otherwise and ChatGPT tend to be pleasers, they may try to use your prompt and not move a lot out of it like, this is yours. This is so great. I'm not going to change it. No, I allow you to change it. I allow you to rewrite it completely. And I tried it several times on several prompts, not just my own. People are coming with me with why does my custom chatgpt act so badly?

Let's take your prompt and rewrite it using ChatGPT. So I go with that template and first try, it's amazing. First try you get a prompt that is much, much, much better. And usually it only takes that one iteration for it to work exactly as you wanted it to. So that's my tip.

[Claire Veau]

I, I love it. It's a very professional tip that I will use in my moments where instead I just want to write no in all caps, which you know, I try to pretend that I'm this very, you know, patient and sophisticated and AI friendly prompter but I think the more comfortable I get with it, the more ridiculous my my prompts get. It's a good reminder that structure can help. Well, this has been so fun. Where can we find you and how can we be helpful to you?

[Michal Peled]

You can find me in LinkedIn.

I am LinkedIn.commichal.peled.

And I I work at Honeybook so you can just search for Michal Pellet honeybook and find me. I would really love to connect with anyone who is into automations AI.

New things, whatever. I want to see what you do, I want to see what you're working on. I get constant ideas from other people in LinkedIn, in Facebook, whatever.

I'm there and.

From you. You saw something that I did here and it's striked you with a great idea, a way to improve it. You want to suggest things that I can do better or even if you want more information from me, just feel free to reach out, ask questions. I'll be happy to answer. It's one of my favorite things to talk about my work. So feel free to do that.

[Claire Veau]

Well, you heard it. Drop questions in the comments, connect on LinkedIn and if HoneyBook is hiring, you now know how they search for great candidates.

[Michal Peled]

So if you're yeah, we are hiring. Make sure your profile is well, well structured, well, well made.

[Claire Veau]

You know, maybe maybe use the agent to say I'm applying for this job. Could you find me on LinkedIn as a good match and what would I do to improve it? That's the last tip for how I AI Professional AI girl right here. Well, it was so nice to have you. Thank you so much for showing us our workflows. They're really inspiring and we will see you soon. Thanks.

[Michal Peled]

Thank you so much Claire.

[Claire Veau]

Thanks so much for watching. If you enjoyed this show, please like and subscribe here on YouTube or even better leave us a comment with your thoughts. You can also find this podcast on Apple Podcasts, Spotify or your favorite podcast app. Please consider leaving us a rating and review which will help others find the show. You can see all our episodes and learn more about the show@howiai pod.com See you next time.