How I AI

Claire Vo
  • 4.8 (143)
  • 科技
  • 一周一更

How I AI, hosted by Claire Vo, is for anyone wondering how to actually use these magical new tools to improve the quality and efficiency of their work. In each episode, guests will share a specific, practical, and impactful way they’ve learned to use AI in their work or life. Expect 30-minute episodes, live screen sharing, and tips/tricks/workflows you can copy immediately. If you want to demystify AI and learn the skills you need to thrive in this new world, this podcast is for you.

  1. How this CEO turned 25,000 hours of sales calls into a self-learning go-to-market engine | Matt Britton (Suzy)

    1天前

    How this CEO turned 25,000 hours of sales calls into a self-learning go-to-market engine | Matt Britton (Suzy)

    Matt Britton is the founder and CEO of Suzy, a consumer insights platform that has raised over $100 million in venture capital and works with top brands like Coca-Cola, Google, Procter & Gamble, and Nike. Matt is also the bestselling author of YouthNation, a blueprint for understanding the seismic shifts shaping our future economy, and Generation AI, which explores how Gen Alpha and artificial intelligence will transform business, culture, and society. In this episode, Matt demonstrates how he built a comprehensive AI workflow using Zapier that transforms customer call transcripts into a wealth of actionable intelligence. Despite not being a coder, Matt created a system that automatically generates call summaries, sentiment analysis, coaching feedback, follow-up emails, SEO-optimized blog posts, and more—all from a single customer conversation. What you’ll learn: How to build a trigger-based workflow that automatically scrapes and processes customer call transcripts from platforms like GongA systematic approach to quantifying customer sentiment on a 1-10 scale that has proven highly predictive of churn and upsell opportunitiesHow to create an automated coaching system that provides personalized feedback to sales reps after every customer interactionA workflow for extracting keywords from customer conversations to inform Google ad campaigns without manual interventionTechniques for automatically generating privacy-compliant blog content from customer calls that drives organic traffic and paid search performanceWhy CEOs and executives need to build AI skills firsthand rather than delegating implementation to engineering teamsHow to use Google Sheets as structured databases for AI lookups and enrichment within automated workflows— Brought to you by: Brex—The intelligent finance platform built for founders Zapier—The most connected AI orchestration platform — Where to find Matt Britton: LinkedIn: linkedin.com/in/mattbbritton Instagram: https://www.instagram.com/mattbrittonnyc/ Company: https://www.suzy.com/ — 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 — In this episode, we cover: (00:00) Introduction to Matt Britton (02:36) Why Zapier became the backbone of Matt’s AI automations (04:17) Identifying your core business problem (09:02) How Matt built the initial trigger automation with Browse AI (13:42) The value of CEOs getting hands-on with building (14:00) Scraping and processing call transcripts (20:14) Using LLMs to generate call summaries and sentiment scores (23:25) Creating a Slack channel for real-time call insights (26:17) Extracting keywords for Google Ads campaigns (28:35) Building an AI coach for sales and customer success teams (29:48) Creating a follow-up email writer for post-call communication (35:25) Generating redacted blog content from customer conversations (37:51) How this approach changes team building and hiring priorities (40:19) Matt’s prompting techniques and final thoughts — Tools referenced: • Zapier: https://zapier.com/ • Gong: https://www.gong.io/ • Browse AI: https://www.browse.ai/ • ChatGPT: https://chat.openai.com/ — Other references: • Qualtrics: https://www.qualtrics.com/ • SurveyMonkey: https://www.surveymonkey.com/ • Slack: https://slack.com/ • Google Sheets: https://www.google.com/sheets/about/ — Production and marketing by https://penname.co/. For inquiries about sponsoring the podcast, email jordan@penname.co.

    43 分钟
  2. The complete beginner’s guide to coding with AI: from PRD to generating your very first lines of code

    6天前

    The complete beginner’s guide to coding with AI: from PRD to generating your very first lines of code

    This episode is for complete beginners. I walk you through how to build your very first coding project using AI tools—even if you’ve never written a line of code. Together, we’ll create a personal project hub that automatically generates documentation and lets you build interactive prototypes. I’ll show you the process step by step—from setting up a repository, to creating AI agents that help with specific tasks, to deploying a functional web app locally. What you’ll learn: How to set up a simple Next.js application from scratch using Cursor’s AI agent capabilities My workflow for creating AI agents that generate consistent documentation (like PRDs in Markdown format) How to build and display clickable prototypes without worrying about complex backend functionality The basics of using GitHub to track changes and manage your code repository as a non-technical person Why starting with a personal project hub is the best way to ease into AI-assisted coding My favorite practical tips for iterating on designs and functionality using AI tools—without needing deep technical expertise — Brought to you by: ChatPRD—An AI copilot for PMs and their teams — In this episode, we cover: (00:00) Introduction (05:11) Starting with a requirements document in ChatPRD (08:22) Attempting to use v0 for initial prototyping (15:02) Pivoting to Cursor for initial prototyping (20:20) Running the app locally and reviewing the initial version (24:07) Setting up GitHub for version control (27:09) Creating an AI agent for writing PRDs (31:04) Using the agent to create a sample PRD (35:00) Building a prototype based on the PRD (37:00) Testing and improving the prototype (40:00) Adding documentation and improving the design (43:20) Recap of the complete workflow — Tools referenced: • Cursor: https://cursor.com/ • ChatPRD: https://www.chatprd.ai/ • v0: https://v0.dev/ • GitHub Desktop: https://desktop.github.com/ • Next.js: https://nextjs.org/ • Tailwind CSS: https://tailwindcss.com/ — Other references: • Lovable: https://lovable.ai/ • Bolt: https://bolt.new/ • Claude Code: https://www.claude.com/product/claude-code • Markdown: https://www.markdownguide.org/ • GitHub: https://github.com/ — Production and marketing by https://penname.co/. For inquiries about sponsoring the podcast, email jordan@penname.co.

    45 分钟
  3. “Vibe analysis”: How Faire’s data team uses AI to investigate conversion drops, analyze experiment results, and convert raw data into executive-ready insights

    11月3日

    “Vibe analysis”: How Faire’s data team uses AI to investigate conversion drops, analyze experiment results, and convert raw data into executive-ready insights

    Tim Trueman and Alexa Cerf from Faire’s data team demonstrate how AI tools are revolutionizing data analysis workflows. They show how data teams, product managers, and engineers can use tools like Cursor, ChatGPT, and custom agents to investigate business metrics, analyze experiment results, and extract insights from user surveys—all while dramatically reducing the time and technical expertise required. What you’ll learn: 1. How to use AI to investigate sudden drops in business metrics by searching documentation and codebases 2. Techniques for creating a semantic layer that helps AI understand your business data 3. How to build end-to-end analytics workflows using Cursor and Model Context Protocols (MCPs) 4. Ways to automate experiment analysis and create standardized reports 5. How AI can help design and analyze customer surveys 6. Strategies for creating executive-ready documents from raw data analysis 7. Why every team member should have access to code repositories—not just engineers — Brought to you by: Zapier—The most connected AI orchestration platform Brex—The intelligent finance platform built for founders — Where to find Tim Trueman: LinkedIn: https://www.linkedin.com/in/tim-trueman-99788592/ — Where to find Alexa Cerf: LinkedIn: https://www.linkedin.com/in/alexandra-cerf/ — 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 — In this episode, we cover: (00:00) Introduction to Tim and Alexa from Faire (02:53) The challenge of analyzing product quality and usage (04:14) Breaking down what analytics actually involves beyond data manipulation (05:46) Demo: Investigating a conversion rate drop using enterprise AI search (09:05) Using ChatGPT Deep Research to analyze code changes (12:40) Leveraging Cursor as the ultimate context engine for code analysis (18:55) Analyzing a new product feature’s performance with Cursor (26:27) How semantic layers make AI tools more effective for data analysis (30:00) Using Model Context Protocols (MCPs) to connect AI with data tools (34:17) Creating visualizations and dashboards with Mode integration (37:04) Generating structured analysis documents with Notion integration (44:39) Building custom agents to automate experiment result documentation (53:10) Designing and analyzing customer surveys (59:40) Lightning round and final thoughts — Tools referenced: • Cursor: https://cursor.com/ • ChatGPT: https://chat.openai.com/ • Notion: https://www.notion.so/ • Snowflake: https://www.snowflake.com/ • Mode: https://mode.com • Qualtrics: https://www.qualtrics.com/ • GitHub: https://github.com/ — Other references: • Model Context Protocol (MCP): https://www.anthropic.com/news/model-context-protocol • Faire Careers: https://www.faire.com/careers — Production and marketing by https://penname.co/. For inquiries about sponsoring the podcast, email jordan@penname.co.

    1 小时 3 分钟
  4. Vibe-coding a kid-friendly AI fortune teller for your Halloween festivities | Marco Casalaina (Microsoft VP)

    10月31日

    Vibe-coding a kid-friendly AI fortune teller for your Halloween festivities | Marco Casalaina (Microsoft VP)

    In this impromptu Halloween special, Marco Casalaina (VP of Products for Core AI at Microsoft) demonstrates how he uses GitHub Spark to quickly build a mobile app that generates kid-friendly fortunes for trick-or-treaters. — Where to find Marco Casalaina: LinkedIn: https://www.linkedin.com/in/marcocasalaina/ X: https://x.com/amrcn_werewolf?lang=en — 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 — In this episode, we cover: (00:00) Intro (00:40) Marco’s Halloween fortune teller tradition (02:54) Using GitHub Spark to create a fortune teller app (04:32) Using Spec Kit for scoping out complex feature specs (06:53) Making fortunes more concrete and kid-friendly (10:20) Closing thoughts — Tools referenced: • GitHub Spark: https://github.com/features/spark • SpecKit: https://github.com/github/spec-kit • GitHub Copilot: https://github.com/features/copilot • Cursor: https://cursor.com/ • Claude Code: https://www.claude.com/product/claude-code — Production and marketing by https://penname.co/. For inquiries about sponsoring the podcast, email jordan@penname.co.

    12 分钟
  5. “Cursor is a much better product manager than I ever was”: How this PM uses AI for PRDs, Jira tickets, and replying to coworkers | Dennis Yang (Chime)

    10月27日

    “Cursor is a much better product manager than I ever was”: How this PM uses AI for PRDs, Jira tickets, and replying to coworkers | Dennis Yang (Chime)

    Dennis Yang is the Principal Product Manager for Generative AI at Chime, where he’s pioneered AI workflows that meaningfully increase productivity. While most people use Cursor as a coding tool, Dennis has turned it into a comprehensive product-management system that automates PRD creation, documentation management, ticket creation, status reporting, and even comment responses—without writing code. In this episode, he shares his end-to-end workflow and how non-technical professionals can leverage AI-powered IDEs. What you’ll learn: Why Cursor is the perfect hub for product management (even if you don’t code)How to use MCPs (Model Context Protocols) to push content between Cursor, Confluence, and NotionThe workflow for creating PRDs in Cursor and automatically responding to commentsHow to automate Jira ticket creation directly from your PRDsA system for generating comprehensive status reports without manual workHow to prototype AI products in minutes using Cursor as a “super MVP” environmentWhy source-controlled markdown files might replace traditional SaaS tools— Brought to you by: Zapier—The most connected AI orchestration platform Brex—The intelligent finance platform built for founders — Where to find Dennis Yang: Twitter/X: https://twitter.com/sinned LinkedIn: https://www.linkedin.com/in/dennisyang/ Chime: https://www.chime.com/ — 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 — In this episode, we cover: (00:00) Introduction to Dennis Yang (03:00) Why Cursor is ideal for product management workflows (04:53) Setting up Cursor for non-coding use cases with markdown preview (09:35) Creating PRDs in Cursor and using source control for documentation (10:33) Using MCPs to publish content to Confluence and Notion (11:38) Bridging the gap between engineering and product (17:00) Reading and responding to document comments with AI assistance (21:37) Creating comprehensive Jira tickets directly from PRDs (25:51) Generating automated status reports from Jira data (30:23) Building a morning briefing system with ChatGPT (35:03) Generating personal morning briefings using ChatGPT (40:04) The “super MVP” approach to AI product development (46:37) Lightning round and final thoughts — Tools referenced: • Cursor: https://cursor.com/ • Confluence: https://www.atlassian.com/software/confluence • Notion: https://www.notion.so/ • Jira: https://www.atlassian.com/software/jira • ChatGPT: https://chat.openai.com/ • Claude: https://claude.ai/ • Git: https://git-scm.com/ — Other references: • News API: https://newsapi.org/ • Semrush: https://www.semrush.com/ — Production and marketing by https://penname.co/. For inquiries about sponsoring the podcast, email jordan@penname.co.

    50 分钟
  6. Claude Skills explained: How to create reusable AI workflows

    10月22日

    Claude Skills explained: How to create reusable AI workflows

    Today I dive into Anthropic’s latest feature that lets anyone create reusable workflows for Claude—no coding required. I break down exactly what Claude Skills are, how to build them from scratch, and how to use them inside Claude Code and Cursor to automate recurring AI tasks like generating PRDs, writing changelog summaries, and turning demo notes into follow-up emails. What you’ll learn: What Claude Skills are and how they differ from Claude Projects and custom GPTsHow to structure a Skill (metadata, instructions, and linked files)Why defining workflows in natural language beats rigid automation toolsHow to create Claude Skills using Claude Code and CursorHow to validate your skills with Python scripts and folder referencesHow to upload and use Claude Skills inside Claude’s web or desktop appPractical examples: turning changelogs into newsletters, demo notes into emails, and more— Brought to you by: ChatPRD—An AI copilot for PMs and their teams — 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 — In this episode, we cover: (00:00) Introduction (01:39) What are Claude Skills and how do they work? (08:30) The structure of Claude Skills files (11:00) Demo: Creating Skills using Claude’s built-in skill creator (16:08) A more efficient workflow: Creating Skills with Cursor (17:42) Using Python validation scripts (18:37) Testing Skills with Claude Code (20:52) Creating a changelog-to-newsletter Skill (22:16) Creating a demo-to-follow-up-email Skill (23:45) Uploading Skills to the Claude web interface (26:04) Conclusion and summary — Tools referenced: • Claude: https://claude.ai/ • Claude Code: https://claude.ai/code • Cursor: https://cursor.sh/ — Other references: • Equipping agents for the real world with Agent Skills: https://www.anthropic.com/engineering/equipping-agents-for-the-real-world-with-agent-skills • Anthropic Skills Documentation: https://docs.claude.com/en/docs/claude-code/skills?utm_source=chatgpt.com • Claude Projects:https://claude.ai/projects — Production and marketing by https://penname.co/. For inquiries about sponsoring the podcast, email jordan@penname.co.

    27 分钟
  7. How this Yelp AI PM works backward from “golden conversations” to create high-quality prototypes using Claude Artifacts and Magic Patterns | Priya Badger

    10月20日

    How this Yelp AI PM works backward from “golden conversations” to create high-quality prototypes using Claude Artifacts and Magic Patterns | Priya Badger

    Priya Badger, a product manager at Yelp, shares her innovative approach to designing AI-powered products by starting with example conversations rather than traditional wireframes or PRDs. In this episode, she demonstrates how she uses Claude and Magic Patterns to prototype Yelp’s AI assistant features—from exploring conversation flows to designing user interfaces. What you’ll learn: 1. How to use example conversations as your first “wireframe” when designing conversational AI products 2. A step-by-step workflow for using Claude to generate and refine sample conversations that guide your AI product development 3. Techniques for creating interactive prototypes with Claude Artifacts that use real LLM responses without complex API integrations 4. How to use Magic Patterns’ Inspiration mode to rapidly explore multiple UI variations for your AI features 5. Why starting with conversations and working backward to system prompts creates more natural AI interactions 6. How to apply these AI prototyping techniques to personal projects to build your AI product management skills — Brought to you by: GoFundMe Giving Funds—One account. Zero hassle. Persona—Trusted identity verification for any use case — Where to find Priya Badger: LinkedIn: https://www.linkedin.com/in/priyamathewprofile/ Substack: https://almostmagic.substack.com/ — 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 — In this episode, we cover: (00:00) Introduction to Priya (02:54) The unique challenges of managing AI-powered products (04:33) Using example conversations as a starting point for design (05:53) Demo: Prompting Claude to generate sample conversations (09:10) Prototyping advice (09:53) Testing with multiple example images and scenarios (15:03) Refining conversations based on qualitative assessment (15:59) Demo: Creating interactive prototypes with Claude Artifacts (21:22) Using Magic Patterns to design the user interface (25:30) Exploring multiple design variations with Inspiration mode (31:02) Quick summary (33:35) How to apply these AI prototyping techniques to personal projects (38:57) Final thoughts — Tools referenced: • Claude: https://claude.ai/ • Magic Patterns: https://magicpatterns.com/ • Lovable: https://lovable.ai/ • Figma: https://www.figma.com/ • ChatGPT: https://chat.openai.com/ — Other references: • How to build prototypes that actually look like your product | Colin Matthews (product leader, AI prototyping instructor at Maven): https://www.lennysnewsletter.com/p/how-to-build-prototypes-that-actually — Production and marketing by https://penname.co/. For inquiries about sponsoring the podcast, email jordan@penname.co.

    42 分钟
  8. Evals, error analysis, and better prompts: A systematic approach to improving your AI products | Hamel Husain (ML engineer)

    10月13日

    Evals, error analysis, and better prompts: A systematic approach to improving your AI products | Hamel Husain (ML engineer)

    Hamel Husain, an AI consultant and educator, shares his systematic approach to improving AI product quality through error analysis, evaluation frameworks, and prompt engineering. In this episode, he demonstrates how product teams can move beyond “vibe checking” their AI systems to implement data-driven quality improvement processes that identify and fix the most common errors. Using real examples from client work with Nurture Boss (an AI assistant for property managers), Hamel walks through practical techniques that product managers can implement immediately to dramatically improve their AI products. What you’ll learn: 1. A step-by-step error analysis framework that helps identify and categorize the most common AI failures in your product 2. How to create custom annotation systems that make reviewing AI conversations faster and more insightful 3. Why binary evaluations (pass/fail) are more useful than arbitrary quality scores for measuring AI performance 4. Techniques for validating your LLM judges to ensure they align with human quality expectations 5. A practical approach to prioritizing fixes based on frequency counting rather than intuition 6. Why looking at real user conversations (not just ideal test cases) is critical for understanding AI product failures 7. How to build a comprehensive quality system that spans from manual review to automated evaluation — Brought to you by: GoFundMe Giving Funds—One account. Zero hassle: https://gofundme.com/howiai Persona—Trusted identity verification for any use case: https://withpersona.com/lp/howiai — Where to find Hamel Husain: Website: https://hamel.dev/ Twitter: https://twitter.com/HamelHusain Course: https://maven.com/parlance-labs/evals GitHub: https://github.com/hamelsmu — 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 — In this episode, we cover: (00:00) Introduction to Hamel Husain (03:05) The fundamentals: why data analysis is critical for AI products (06:58) Understanding traces and examining real user interactions (13:35) Error analysis: a systematic approach to finding AI failures (17:40) Creating custom annotation systems for faster review (22:23) The impact of this process (25:15) Different types of evaluations (29:30) LLM-as-a-Judge (33:58) Improving prompts and system instructions (38:15) Analyzing agent workflows (40:38) Hamel’s personal AI tools and workflows (48:02) Lighting round and final thoughts — Tools referenced: • Claude: https://claude.ai/ • Braintrust: https://www.braintrust.dev/docs/start • Phoenix: https://phoenix.arize.com/ • AI Studio: https://aistudio.google.com/ • ChatGPT: https://chat.openai.com/ • Gemini: https://gemini.google.com/ — Other references: • Who Validates the Validators? Aligning LLM-Assisted Evaluation of LLM Outputs with Human Preferences: https://dl.acm.org/doi/10.1145/3654777.3676450 • Nurture Boss: https://nurtureboss.io • Rechat: https://rechat.com/ • Your AI Product Needs Evals: https://hamel.dev/blog/posts/evals/ • A Field Guide to Rapidly Improving AI Products: https://hamel.dev/blog/posts/field-guide/ • Creating a LLM-as-a-Judge That Drives Business Results: https://hamel.dev/blog/posts/llm-judge/ • Lenny’s List on Maven: https://maven.com/lenny — Production and marketing by https://penname.co/. For inquiries about sponsoring the podcast, email jordan@penname.co.

    55 分钟
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关于

How I AI, hosted by Claire Vo, is for anyone wondering how to actually use these magical new tools to improve the quality and efficiency of their work. In each episode, guests will share a specific, practical, and impactful way they’ve learned to use AI in their work or life. Expect 30-minute episodes, live screen sharing, and tips/tricks/workflows you can copy immediately. If you want to demystify AI and learn the skills you need to thrive in this new world, this podcast is for you.

信息

  • 创作者
    Claire Vo
  • 活跃年份
    2025年
  • 单集
    35
  • 分级
    儿童适宜
  • 版权
    © Claire Vo
  • 节目网站
    How I AI

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