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ChatGPT agent mode: The “little helper” that transformed recruiting & solved parking nightmares

Michal Peled is a Technical Operations Engineer at HoneyBook who specializes in building internal tools and automations that eliminate friction for teams. In this episode, Michal demonstrates three practical AI use cases: using ChatGPT’s agent mode to automate LinkedIn recruiting, transforming customer research into interactive AI personas, and creating a custom calendar solution for a very San Francisco–specific problem—avoiding expensive parking during Giants games. *What you’ll learn:* 1. How to use ChatGPT agent mode to automate LinkedIn recruiting and find high-quality candidates that manual searches missed 2. The step-by-step process for turning static customer research into interactive AI personas that product and marketing teams can actually use 3. Why NotebookLM excels at creating prompts from source material with proper citations 4. How to structure agent-mode prompts to create effective “little helpers” that follow your exact workflow 5. A practical framework for improving your prompts when AI tools aren’t giving you the results you want 6. How internal tools teams can drive massive impact by focusing on eliminating friction in everyday workflows *Brought to you by:* Brex—The intelligent finance platform built for founders: https://brex.com/howiai Google Gemini—Your everyday AI assistant: https://ai.dev/ *In this episode, we cover:* (00:00) Introduction to Michal and ChatGPT agent mode (02:10) Using agent mode for LinkedIn recruiting automation (05:14) Creating effective prompts for agent mode (10:50) Demo of agent mode searching LinkedIn profiles (16:29) Results and team reception of the recruiting automation (19:53) The outcome of implementing on Michal’s team (23:50) Creating custom GPT personas from customer research (28:43) Using NotebookLM to transform research into persona prompts (35:00) Adding guardrails to custom GPT personas (37:20) Demo of interacting with custom-persona GPTs (41:02) Creating a calendar automation for parking during baseball games (48:15) Lightning round and final thoughts *Tools referenced:* • ChatGPT: https://chat.openai.com/ • NotebookLM: https://notebooklm.google.com/ • Claude: https://claude.ai/ *Other references:* • Google Calendar: https://calendar.google.com/ • HoneyBook: https://www.honeybook.com/ • LinkedIn: https://www.linkedin.com/ *Where to find Michal Peled:* LinkedIn: https://www.linkedin.com/in/michalpeled/ *Where to find Claire Vo:* ChatPRD: https://www.chatprd.ai/ Website: https://clairevo.com/ LinkedIn: https://www.linkedin.com/in/clairevo/ X: https://x.com/clairevo _Production and marketing by https://penname.co/._ _For inquiries about sponsoring the podcast, email jordan@penname.co._

Claire VohostMichal Peledguest
Dec 7, 202558mWatch on YouTube ↗

At a glance

WHAT IT’S REALLY ABOUT

ChatGPT agent mode automates recruiting, personas, and parking planning workflows

  1. The episode introduces ChatGPT “agent mode” as a practical way to offload repetitive, high-friction work by letting ChatGPT browse sites, log in, and take actions with human handoff points.
  2. Michal shows a recruiting automation where an agent logs into LinkedIn, searches against an uploaded job description, applies hiring-team constraints, and returns a ranked shortlist of candidates in minutes.
  3. Next, he turns expensive customer research documents into five interactive buyer-persona GPTs, using NotebookLM for source-grounded prompt drafting and then adding guardrails to keep personas accurate and safe.
  4. Finally, he uses ChatGPT to generate a filtered baseball-game schedule and output an ICS calendar that warns employees about Oracle Park game days that trigger parking price spikes.

IDEAS WORTH REMEMBERING

5 ideas

Treat agent mode like a coworker with a clear job description.

Michal frames prompts as delegating to a “little helper” (e.g., “You are an IT recruiter”), then spells out the exact workflow steps a human would take, which improves consistency and results.

Add explicit handoff points for secure, collaborative automation.

For sensitive steps like logging into LinkedIn, the prompt instructs the agent to pause and let the user take control, keeping the flow practical while still automated end-to-end.

Restrictions beat general instructions for repeatable sourcing quality.

Candidate constraints (location, recent activity, seniority, tenure/unemployment windows) were taken from the hiring team’s real process, making the agent’s search behavior match how the team actually screens.

A “match score” makes AI outputs more usable, even if imperfect.

Requesting a percent match helps recruiters quickly compare candidates and decide where to inspect deeper; it’s not exact science, but it’s a helpful prioritization layer.

Persona GPTs work best when the persona lives in instructions, not PDFs.

Michal found that simply uploading research leads to answers *about* the persona; tightly written instructions (“you are this person…belief system…decision style…tone”) produces answers *as* the persona.

WORDS WORTH SAVING

5 quotes

I want a little helper. I'm a recruiter. I want someone who is like me.

— Michal Peled

If you can codify what a person's step-by-step workflow is… you can replicate and automate that at scale.

— Claire Vo

Don't add or modify text that is not written or implied in the text. Okay, I know you're creative. I'm turning you down.

— Michal Peled

Out of these five, four of them were never found by us manually… and they really fit the description.

— Michal Peled

It can ruin your entire day, for sure.

— Michal Peled

ChatGPT agent mode setup and UXLinkedIn sourcing automation with login and actionsPrompt structure: roles, tasks, restrictions, thresholdsScored/ranked outputs for decision supportPersona GPT creation from customer researchNotebookLM for grounded prompt generation with citationsICS calendar automation for real-world planning

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