I recently revisited a topic I once pushed back on: using AI to analyze (and maybe even synthesize) customer interviews. After six months of real-world experiments and countless conversations with seasoned product leaders, I’ve evolved my perspective. There is meaningful value here—but only when we’re clear about where AI helps and where it quietly erodes the hard-won customer understanding that powers great product decisions.
If you want to experience the conversation that sparked this reflection, you can listen to the episode on Spotify or Apple Podcast, and watch the discussion here: YouTube. It’s a candid, practical exploration of AI’s role in continuous discovery, and it mirrors what I’m seeing on the ground with product trios and empowered product teams.
Here’s the crux: AI raises the floor for beginners but accelerates experts even more. That matches my experience—early-career PMs get structure, momentum, and a confidence boost, while experienced interviewers can move faster without sacrificing nuance. But there’s a catch. If your interviewing skills aren’t solid yet, AI can create a veneer of insight that masks shallow understanding. In other words, it can help you go wrong more efficiently.
The conversation makes an important distinction between analysis and synthesis. Analysis is about extracting signals from the interview. Synthesis is about building meaning—connecting patterns, weighing contradictions, and deciding what to do next. AI can speed up the former with summaries and highlights. The latter—true synthesis—still demands expert judgment, context, and empathy.
One line from the episode stuck with me: your unpolished interview skills matter more than any shiny new AI workflow. I’ve felt that firsthand. When interview quality is uneven, dropping transcripts into an LLM won’t save you. You still need to synthesize every interview individually so the signals remain traceable and credible. That discipline keeps teams aligned, prevents overfitting to noise, and builds the organizational memory that fuels better bets.
We also explored the operational reality most teams face: interviews pile up. Backlogs grow. Leaders want speed. This is where “expert + AI” shines. With the right prompts, templates, and context, tools like ChatGPT and Claude can help transform raw transcripts into structured artifacts you can trust—provided a strong interviewer sets the frame and makes the calls. That balance preserves both velocity and quality.
What changed my mind most was the evidence from experiments—running sets of interviews through different LLMs and comparing outcomes. The patterns were consistent: beginner + AI is usually better than nothing, but the real performance gains come from expert + AI. When experts guide the process, AI becomes an accelerant rather than a crutch.
A favorite story in the episode takes a detour into building a gaming PC—an unexpected but perfect metaphor for AI’s limits. You can get great step-by-step guidance from a model, but when context shifts or edge cases appear, expertise is what keeps you from making expensive mistakes. Customer interviews are like that. Empathy comes from human interaction; AI can’t replace the experience of talking directly to your customers.
My practical guidance for teams integrating AI into continuous discovery: start with interviewing fundamentals, separate analysis from synthesis, and standardize how you capture single-interview learnings. If you need a tight template for this, refer to “The Interview Snapshot: How to Synthesize and Share What You Learned from a Single Customer Interview.” Use AI for summaries, clustering, and draft artifacts—but have an expert finalize the narratives, evaluate trade-offs, and document assumptions.
If you’re scaling this across an organization, invest in training first, then in workflows. Build a lightweight operating system for discovery: consistent interview guides, “story-based” techniques, and a shared library of prompts. Consider resources like “The Interview Coach,” as well as practical write-ups such as “Customer Interview Analysis: Where AI Helps and Hurts.” These help teams avoid common pitfalls and make better use of AI in high-judgment moments.
My bottom line: AI isn’t magic. It can help, but only if your interviews are strong and you provide the right context. Customer understanding is a competitive moat; outsourcing it entirely will cost you in the long run. Use AI to accelerate—not replace—the human judgment that makes product discovery work.
Resources and links worth exploring: ChatGPT, Claude, The Interview Snapshot: How to Synthesize and Share What You Learned from a Single Customer Interview, The Interview Coach, and Customer Interview Analysis: Where AI Helps and Hurts.
I’d love to hear how your team is using AI in discovery. What’s working, what’s risky, and where do you draw the line between automation and judgment? Share your experiences in the comments—our community learns faster when we compare notes.
Every week, I lean on ChatGPT to cut through noise, reduce rework, and move faster with more confidence. It’s not a silver bullet, but it has become an unfair advantage in my day-to-day leadership of product strategy, discovery, and delivery. Unlock workflows, prompts, and real PM tips showing how ChatGPT quietly reshapes product management behind the scenes.
Here’s my stance: ChatGPT doesn’t replace product judgment. It amplifies it. Used well, it accelerates product discovery, clarifies roadmaps, sharpens positioning, and strengthens stakeholder management. Used poorly, it creates noise and risk. What follows are the specific workflows and prompts that reliably save me hours while protecting quality and trust.
Discovery and research are where I see the biggest upside. I use ChatGPT to draft interview guides, transform raw notes into theme clusters, and generate “Jobs to Be Done” problem statements—then I validate them with customers. I anonymize inputs to protect privacy and follow privacy-by-design and data governance commitments; AI risk management matters more than ever when we’re handling real user data.
When I move from insight to definition, ChatGPT helps me spin up crisp PRDs and user stories. I provide context about our users, constraints, and success metrics and ask for structured outputs: goals, non-goals, acceptance criteria, and risks. This keeps our product trios aligned and focused on outcomes vs output OKRs, not just shipping features.
For competitive analysis and positioning, I feed in public information and ask for points of parity, points of differentiation, and potential messaging angles. I treat the output as a starting point for my value proposition and battlecards—not the final word. It’s a fast way to surface hypotheses and pressure-test our product-led growth narrative.
Roadmapping and sprint planning also benefit. I use ChatGPT to map dependencies, draft milestone narratives, and transform epics into well-formed backlogs. When we align quarterly plans, I ask for risk scenarios and contingency options so we can make trade-offs explicit before we commit.
On analytics and experiments, ChatGPT is my drafting partner. It helps me define A/B testing plans, clarify the minimum detectable effect (MDE), and outline instrumentation requirements. I still verify numbers in our analytics stack, but the scaffolding is done in minutes, not hours—freeing me to focus on retention analysis and activation levers.
Stakeholder communication is where the time savings compound. I use ChatGPT to produce executive summaries, QBRs vs OKRs comparisons, and board-ready narratives that highlight outcomes, risks, and next steps. It’s a powerful way to stay crisp and consistent across leadership updates without losing the nuance that matters.
Prompt patterns make or break results. I keep four rules: set the role, provide rich context, define constraints, and specify the output format. For example: “You are a senior PM advisor. Context: [user, market, problem]. Constraints: [privacy, timeline, budget]. Output: PRD with goals, acceptance criteria, and risks.” With larger inputs, I use context window management by chunking content and asking for summaries before synthesis.
For internal knowledge, I lean on a retrieval-first pipeline. Instead of pasting long docs, I reference curated, approved sources so answers track to current reality. CustomGPT workflows and a simple ChatGPT connector help with governance: they increase speed while reducing the chance of hallucinations and stale information.
Guardrails are non-negotiable. We never paste sensitive data into prompts; we redact PII, spot-check against source-of-truth systems, and red-team important outputs. AI risk management isn’t just a checkbox—it’s how we maintain trust while scaling productivity with gen ai.
Finally, enablement turns personal productivity into team capability. I run short playbooks for empowered product teams: discovery synthesis, PRD drafting, roadmap storytelling, and stakeholder-ready updates. The result is higher-quality thinking, faster cycles, and fewer meetings to align on the essentials.
ChatGPT for product managers isn’t hype; it’s a practical edge when you apply discipline. Start with one workflow that drains your time, add a prompt template, and measure the outcome. In a week, you’ll have proof. In a quarter, you’ll have a new operating system for how your team learns, decides, and ships.
I’ve spent much of my career compressing the distance between a napkin sketch and something real customers can touch. At HighLevel, my product teams use generative AI to validate ideas faster, reduce risk earlier, and win stakeholder trust with evidence instead of slides. The goal isn’t to be flashy—it’s to be precise, testable, and repeatable.
Today, you can build it before you pitch it. AI prototyping can turn ideas into clickable demos in hours. Here are some tools to try and steps to follow.
I start every AI prototyping sprint by sharpening the problem statement and the outcome we care about. That means being explicit about the target user, jobs-to-be-done, and the riskiest assumptions. I define a minimum detectable effect (MDE) and tie it to outcomes vs output OKRs so everyone aligns on what “good” looks like before we touch a tool.
From there, I move from sketch to interface. I capture a rough flow (whiteboard, tablet, or even paper) and generate UI variations with my AI product toolbox—tools that translate structure into components and screens. I’ll iterate on information hierarchy and copy until the narrative supports the core job, borrowing techniques from UX writing. For product managers leaning into LLMs for product managers, this phase is about speed to feedback, not perfection.
Next, I wire data and logic. I connect a lightweight backend or spreadsheet, stitch in a CRM integration if needed, and add LLM calls through a ChatGPT connector or Claude Code. If the concept benefits from multi-step autonomy, I introduce agentic AI to orchestrate tasks across APIs. CustomGPT workflows help me encapsulate business rules so the demo behaves consistently in user paths we care about.
Governance is not optional at this stage. I apply privacy-by-design defaults, document data governance decisions, and run a quick AI risk management pass: input validation, prompt safety, rate limits, and fallback responses. This keeps the prototype credible and prevents false positives from polluting stakeholder perception.
With a click-through in hand, I instrument the experience so learning compounds. I drop in Amplitude analytics to track activation, task completion, and drop-off, and set up simple A/B testing when there’s a meaningful design or copy choice. This makes the prototype a learning vehicle, not just a demo.
Then I get it in front of users—fast. Five targeted conversations will beat fifty internal opinions. I run structured product discovery interviews, observe time-to-value, and capture objections. This is where empowered product teams shine: we make changes in real time, re-run the flow, and document what moves the needle for product-led growth.
When speed matters, I use a four-hour cadence: Hour 1 for problem framing and MDE; Hour 2 for sketch-to-UI generation; Hour 3 for data wiring and AI logic; Hour 4 for instrumentation and user walkthroughs. By the end, we have a clickable demo, preliminary analytics, and a clear decision on whether to advance, pivot, or park.
Finally, I translate insights into a concise artifact: the hypothesis we tested, the signal we observed, the trade-offs we made, and the next sprint plan for product roadmapping and sprint planning. The point is not to be right on the first try; it’s to learn precisely, cheaply, and quickly enough to invest with conviction.
If you adopt this approach, you’ll find that stakeholder management becomes easier, team energy rises, and your roadmap earns credibility. Build it before you pitch it, and let real interactions—not wishful thinking—do the heavy lifting.
"Can you critique the landing page for my new Story-Based Customer Interviews course?" That simple ask used to kick off hours of back-and-forth where I fed an AI the same context over and over—only to get generic feedback that wouldn’t land with my audience or fit my products. As a product leader, that inefficiency was unacceptable; as a writer, it was just plain frustrating.
Not anymore. Today, Claude not only critiques my work, it helps me produce it. It generates marketing copy—in my voice. It helps me write blog posts. It knows what search terms are relevant to my business and helps me optimize my articles for SEO and now AEO. It helps me with competitive research, academic research, and discovery research. And it does all of this with little prompting from me.
I don’t upload files to a web-based project. I don’t manage elaborate prompt libraries. I don’t repeat myself. I ask for help and Claude knows exactly what to do. The shift happened when I learned how to give Claude Code a memory. Claude now knows who my target customer is, the key value propositions I focus on, the specific opportunities each product addresses, my revenue model, my marketing channels, and so much more.
A dark-themed strategy slide for the post Stop Repeating Yourself: Give Claude Code a Memory, showing how to lead with a CLAUDE.md glossary page, write clearly for nontechnical readers, and link glossary and article to boost discovery and engagement.
With that memory, I consistently get high-quality output tailored to my audience and aligned to my products and services. I don’t retype the same context; Claude just remembers. In this article, I’ll show you exactly how I set up that memory. It relies on Claude Code (which requires a Pro subscription), and it’s worth it. If you’re new to Claude Code, start with "Claude Code: What It Is, How It’s Different, and Why Non-Technical People Should Use It."
Here’s the underlying problem: with large language models, every conversation starts from scratch. Yes, ChatGPT can remember some things and Claude can search past conversations, but practically speaking each new thread wipes the slate clean. If I were working on a new landing page, I’d normally need to upload target customer context, product details, primary and secondary value propositions, FAQ questions and answers, plus testimonials and logos for social proof—every single time.
Start fast with Claude’s home screen: Sonnet 4.5 is ready, and quick actions for writing, learning, and coding sit beneath a clean prompt box—ideal for showing how memory cuts repetition and streamlines daily development.
Projects in web-based tools help a bit, but they introduce a new dilemma. When I move to the next landing page targeting the same customer but a different product and value proposition, do I start a new Project (tedious) or keep expanding the old one (which muddies the context window and degrades output quality)? The good news: Claude Code solves this by giving the model a precise, durable memory without overloading any single conversation.
Claude Code can read files on my local machine, which is an understated superpower. I use those files to create a persistent, reusable memory that works across all chats and Projects. Files can be mixed and matched, so I give Claude exactly what it needs for the task at hand—and nothing more. For a first landing page, I reference the target customer and the relevant product; for the second, I reuse the same target customer file and point to the new product file.
Dark-mode Notes screenshot captures Claude Code in action: it fetches producttalk.org, reads context files, and delivers a concise homepage evaluation—showing how memory streamlines repeated analysis tasks.
When you give an LLM the exact right context, output quality jumps. More context only helps if it’s the right context. For a landing page, Claude needs to know about the current product and perhaps related products for differentiation—but it doesn’t need to know about unrelated offerings. Structure your memory so Claude gets precisely what’s required.
Once I did this, Claude shifted from “intern who needs handholding” to trusted advisor and capable teammate. It doesn’t guess at my value propositions—I’ve already told it. It writes in my voice because it has my writing guide and samples. It knows who owns which course and which use cases map to which features. The setup takes a bit of upfront work, but it compounds: update a file when something changes and you’re done. Most of this information already lives in your system; the trick is making it easy for Claude to use.
See how Claude Code stops repetition: global and project CLAUDE.md files, plus custom reference docs, flow into the editor so the assistant remembers your preferences and context while you code and run commands.
Because the files live on my machine, I own the system. No vendor or device lock-in. I decide when and who to share with. I can work with Claude on one project and ChatGPT on another—both can rely on the same file-based memory strategy. It’s an AI strategy that scales with product discovery, accelerates go-to-market content, sharpens competitive differentiation, and supports product-led growth.
Here’s how I design the memory: I use three layers. Claude Code already encourages global preferences and Project-specific instructions, but the third layer—reference context—is where the real power lives.
Peek inside a markdown playbook for Claude Code: concise rules for writing, multi-level planning, and clear feedback that turn repeated reminders into reusable memory and smoother, faster coding sessions.
Layer 1: Global Preferences (Always on). The first time I launched Claude Code, I created a CLAUDE.md file at ~/.claude/CLAUDE.md. This is where I keep the cross-project rules of engagement—how I like to work with Claude. Mine includes: Always create a plan for me to review before you start any work; Give me direct feedback (no hedging, no gentle suggestions); Use bullet points for summaries; Ask clarifying questions one at a time so I can give complete answers; No emojis unless I explicitly ask for them. Claude Code automatically loads this file at the start of every session, so I never restate my preferences.
Layer 2: Project-Specific Instructions. Different projects have different rules. In my writing workspace, the Project CLAUDE.md sets the roles (I’m the primary writer; Claude is my thought partner and editor), defines a multi-round review flow (content → structure → accuracy → typos), prioritizes human readability over SEO, and points to my writing style guide. In my task management system, I include how my Trello integration works, file naming conventions for tasks, and how to process research papers into summaries. In my code projects, I specify the technology stack (Node.js vs. Python), testing framework (Jest for Node.js, pytest for Python), code style and conventions, project architecture and directory structure, and which dependencies and libraries to use. Each project directory has its own CLAUDE.md, and Claude automatically loads the relevant file when I’m working there.
Peek inside a markdown playbook for collaborating with Claude—covering session setup, roles, editorial standards, and research steps—to show how saved instructions create consistent results without repeating yourself.
Layer 3: Reference Context (Pull as Needed)—the real power. LLMs have a context window—a limit to how much they can process at once. Even within that limit, loading too much degrades performance due to “context rot.” The remedy is ruthless context management: small, targeted files that load only when needed. Keep CLAUDE.md files concise and focused on rules and workflows. For detailed knowledge, create separate reference files and list them in your CLAUDE.md so Claude knows they exist and when to fetch them. When I ask for help creating a landing page, Claude knows to use my business profile, the product file, and my target customers context.
Here’s what most people miss: you don’t cram everything into global or Project files. You maintain small, reusable reference files that Claude only loads on demand. In my walkthrough, I share exactly which context files I created and why; how I got Claude Code to help me create them; how I break them into small, reusable components so Claude gets precisely what it needs; how I keep everything up to date; and step-by-step instructions so you can set up a similar memory system.
Three project notes funnel into Claude Code, turning reusable context into working output. This visual shows how saving key docs as memory lets the AI pick up where you left off and skip repetitive prompting across tasks.
It’s Monday morning, and my Slack and email are already overflowing with content requests: “Can you review this flow?”; “Can you rewrite this screen?”; “Can you name this feature?” I’m not freshly back from holiday—this is just a regular work week kicking off. If you’ve ever been a solo content designer supporting multiple teams, you’ll recognize the pressure. The pipeline for content in product design is always full, and the demand for expertise never stops.
Fixing this isn’t just a matter of better time management or incremental process tweaks. To truly scale, I needed to extend my reach by bringing AI into the design process—without sacrificing judgment, standards, or quality. That Monday morning, I realized I had to scale my skills, my judgment, and our systems, not just my calendar.
Building AI is fundamentally about building systems. I wanted to use AI to scale myself without devaluing critical thinking or flooding the product with generic, verbose content. I also knew a useful AI tool must do more than spit out microcopy—it has to plug into a system we can continually shape. As a content designer, the system is always the starting point. Strong design systems create strong content standards; then AI agents can produce content that meets those standards at speed, freeing me from the bulk of standardized work. That’s not a threat—it’s an advantage. To instruct AI well, our systems must be well constructed.
I often think about this work like a bakery. You need a recipe before you can make a loaf of bread. Most interface content churns out the same loaf, day in and day out. It’s better for the master bakers to focus on the unique, custom bakes—and how the recipe needs to change. With that mindset, I set out to build an AI content design agent.
Inside the Content Design Agent workspace, a clean chat UI titled VERBI pairs a central prompt box with chips for writing, editing, and reviews, plus clear controls to view permissions and open the agent setup for product teams.
When I started this project back in May 2025, many LLMs still had frustrating limitations. Google Gemini let me build a custom Gem agent, but I couldn’t share it with other users. ChatGPT could be customized, but only with static files: I couldn’t point it to live, updatable URL sources. I settled on Glean for three simple reasons: everyone at the company had access; Glean could access all internal documentation and treat URLs as sources of truth; and its then-new Agents feature made AI search customizable. Configuring an agent in Glean is straightforward—you choose a trigger, a set of prompts, and a set of actions—but first I needed to get the inputs right.
AI agents need focus. We had a wealth of internal information at Intercom, but not all of it was current or reliable. I curated exactly what the agent could access and assembled a tightly governed knowledge collection in Glean. Only essential information made the cut: the Intercom style guide—our definitive house style, including regularly-broken rules like “always write in US English” and “use sentence case everywhere”; tone of voice guidance for how we show up across mediums; a product glossary with hundreds of feature names and writing conventions; a monetization glossary for prices, plans, and add-ons; product marketing messaging guides with positioning for every feature and launch; core research insights across the product; and fin.ai and intercom.com/suite as the official, most up-to-date messaging sources.
This is classic RAG (retrieval-augmented generation) in action, ensuring every answer is grounded in approved sources of truth. With the collection in place, I instructed the agent to prioritize these resources above anything else.
Step into a clean, no-code builder that shows how to assemble a Content Design Agent: kick off with a chat-trigger, run a company search, then respond with expert guidance, all guided by a simple starter checklist.
Then came the fun part—building and branding the agent. “Content Design Assistant” felt bland, so I named it VERBI, a nod to its “verbal” design job. When people interact with VERBI, they usually begin with a question, but the intent varies widely. I defined a set of task prompts to guide expectations and outputs: “Can you write this?”; “Can you edit this?”; “Can you review this?”; “Can you name this?”; “Give me options”; “Give me guidance”; “Give me strategy”; “Give me research.” This mirrors the real breadth of content design, from creation to critique to discovery.
To manage responses, VERBI needed three things: start with a specific task prompt; understand how to draw on the right resources each time; and connect with other systems. With task prompts defined, I wrote a detailed system prompt covering the essentials. Role: you are a content designer, supporting product designers. Employer: Intercom (consisting of Fin AI Agent and our next-gen Helpdesk). Resources: content design collection, research collection, Storybook design system. Tone of voice: follow a specific tone for our UI, adjust the tone for everything else. Components: for UI, use the specific guidelines in our design system only. Use cases: writing, editing, critiquing, naming, researching, and more.
One connection mattered most: our design system, recently rebranded as “Surge.” Surge contains detailed content guidelines for every component in our product UI, from accordions and banners to tabs and tooltips. That granularity took months of human effort to codify, and it paid off. Designers no longer guess how to write for a toggle, a button, or a tooltip—and now VERBI understands and enforces those rules, too. A great content design assistant isn’t just a clever system prompt; it needs deep, component-level guidance to retrieve.
UI documentation showcases the Badge component’s content rules, teaching how to name statuses, define types, and apply color so labels read clearly. A handy visual for building a content design agent and ensuring consistent product messaging.
Accessing the design system wasn’t simple at first. It lives in Storybook, which Glean couldn’t access directly. I started by scraping guidance from Storybook into an HTML file with Cursor and uploading it to VERBI—a functional but clunky workaround that required re-scraping every few days. Then our IT team stepped in. They used the Glean Indexing API to turn Storybook into a live data source. Now VERBI connects to Storybook directly. Ask it something ultra-specific, like the correct date format for Japan, and it returns the right answer. That integration elevated the agent from helpful to indispensable—human-level precision, 24/7, at scale.
With prompts and resources in place, I launched VERBI and pressure-tested it. It was accurate and well-informed most of the time, but like any AI agent, it had quirks. I needed it to act as a gatekeeper, not a brainstorming partner that might bend rules or invent new ones. So I added a few explicit guardrails to the system prompt. Stopping sycophancy: “Inform, challenge, and assist. Never placate. Don’t agree by default. If something’s wrong, say so. Challenge assumptions.” Halting hallucinations: “If you don’t find the information required in our resources, say you don’t know the answer. Don’t guess and don’t give answers based on general knowledge.” Avoiding verbosity: “Keep answers short and to the point. Cut the fluff. Skip all niceties and social padding. Only give longer answers if the user asks you to.” These constraints keep responses crisp, correct, and consistent. Like any living system, the prompt needs occasional tune-ups, but the maintenance is minor compared to the upside.
Where we are now: VERBI has been triggered 700+ times since launch. The benefits are tangible. For me, quality scales without constant policing; repetitive questions about naming, style, or punctuation have dropped significantly. I reclaim time because the agent drafts and checks V1 content across teams, enabling me to focus on higher-impact work. For the design team, iteration is faster, confidence is higher, and strategic clarity improves because shared language and grounded guidelines make decisions easier and more consistent.
I used to spend too much time mopping up basic content mistakes and untangling spaghetti-like UI copy prone to human error. VERBI removes those errors at the source. The real advantage is speed: we get from blank slate to a high-quality first draft quickly, which means we can spend our energy deciding whether the content is right, not just “good enough.” Design is the whole interface—words, visuals, interactions—so reviews now happen with real content, never “copy TBD.” Our principle to sweat the details applies equally whether work is human-made or AI-assisted.
Knee-jerk critiques of AI-driven content design often assume teams generate content from nothing and ship it. In reality, great AI is the outcome of great human decisions and strong systems. Its value is pulling us together faster—getting us to a complete, standards-compliant design we can review as a team before sharing it with the world. That’s how AI helps us win: by turning chaos into consistency, and consistency into velocity.
I recently shared 15 ways I'm using AI at home—from fixing cooking disasters to researching school bonds—and those experiments turned into real skills: learning to chat with large language models (LLMs), providing the right context, verifying results, and more.
Now it’s time to apply those same skills at work. The stakes feel higher, the problems are more complex, and we have to navigate when and how AI is acceptable at work. But the foundation we built at home makes the leap far less intimidating.
My goal is to inspire you to start experimenting (if you aren’t already). Along the way, you’ll add practical techniques to your AI product toolbox.
A clean address form ready for automation: fields for Attention, Address, City, State, ZIP, and Country invite AI-driven autofill, validation, and routing, accelerating workflows and reducing manual typing at work.
Using AI at home taught the basics—prompting, context windows, and hallucinations. At work, I layer in orchestration and automation. Don’t worry; we’ll take it step by step.
To make this actionable, I organize my work use cases by complexity, so you can start at the top and move down as your confidence grows. I group them into five buckets: Translator, Do the Work, Researcher, Writing Partner, and Coding Partner. Everyone can access the first three categories; I reserve the last two for subscribers.
Clear course policies at a glance: switch cohorts up to 14 days before start, transfer a seat to another student until the day prior, and get scaled group discounts for Deep Dive courses, though Fundamentals is excluded.
Translator: I’ll start simple with low-stakes examples that build confidence and momentum.
1) Translate this email for me. My last name is common in both Spanish and Portuguese, so people often assume I speak both. I can get by in Spanish, but not Portuguese. When I get an email in another language, I ask ChatGPT for a translation. I used to use Google Translate, but ChatGPT tends to interpret context better. It’s a quick win that gets you comfortable with LLM interactions.
Curious which formats perform best? These heatmaps compare category averages for impressions, engagements, and new followers—spotlighting podcasts for reach and 'Other' for follower gains.
2) Parse this address for me. I live in the United States and work with companies around the world. In Xero, I have to enter addresses by street, city, state/region, country, and zip code. For international addresses, I’m not always sure how to parse fields. ChatGPT is great at this, so I created a CustomGPT to avoid rewriting the prompt. I paste the address, and it returns values mapped to Xero’s fields. If you’re new to CustomGPTs, think of them as reusable prompt-and-context bundles you can share with colleagues. Skills I built: when to use a CustomGPT versus an ad hoc prompt, and how to templatize repetitive formatting tasks.
Do the Work: This is where the magic shows up—AI accelerates execution—provided you set clear guardrails and keep humans in the loop where quality matters.
This concise social post tackles the “no differentiation” myth in B2B, highlighting how segmentation, team alignment, and a clear view of competitors reveal real product value—prompting readers to reflect and join the discussion.
3) Customer service assistant. My company offers a range of products and services, so we created a knowledge base with common questions and template answers to train support. But finding the right response in the moment is slow. I uploaded our content into a CustomGPT and instructed it to surface the most relevant templates, given an inbound email. The key decision: I did not let the model draft final replies. My admin uses suggestions to respond faster, but she remains responsible for the email content. Skills I built: discerning where human oversight is essential and using LLMs to speed up, not outsource, attention-intensive work.
4) Social media analysis. I share my work on social channels and want to know what resonates. LinkedIn lets me export analytics on top posts. Each month I export the last 30 days, ask a CustomGPT to create topic and category heat maps for impressions, engagements, and followers, and I chart trends over time. Patterns become obvious—personal stories drive impressions and engagement; short-form video drives followers. This workflow, inspired by Andy Crestodina at Orbit Media, turns raw analytics into actionable content strategy. Skills I built: using LLMs for data analysis and visualization, moving from exports to insights, and spotting outliers at a glance.
An AI-powered contract review snapshot flags risky clauses and where to push back. Clear labels—Dealbreaker, Needs Redlining, None Found—help teams tighten IP rights, social media controls, refund terms, and injunctive relief.
5) Article summaries. I used to share Worthy Reads—recommended articles—on LinkedIn and X, and I wanted stronger summaries. I asked Claude to generate them in the author’s voice, not “LLM voice.” I gave tone and style guidelines, writing samples, and a clear structure. Quality improved with each iteration. To save time, I automated the workflow with a Zapier zap: when I add a new article to my database, the Anthropic API generates a draft summary and emails it to me for a quick human review. If it looks good, I do nothing. If not, edits are one click away. Skills I built: providing precise context for tone and structure, creating a simple automation, and keeping a light human-in-the-loop review for quality.
6) ContractBot. I regularly review long legal documents and dislike every minute of it, so I built ContractBot as a CustomGPT. It started with a one-sided contract full of red flags—intellectual property, morality clauses, payment terms, and more. I asked ChatGPT to identify issues, we worked through them, and then I had ChatGPT write the reusable prompt that became ContractBot. Now I upload any new contract and get a summary of redlines tailored to my preferences. When new issues arise, I update the CustomGPT prompt, and it evolves with me. Skills I built: iterating preferences over time, using LLMs to translate and revise dense documents, and leveling information asymmetry during negotiations.
Need customer interview guidance fast? This snapshot rounds up five high-ranking guides with quick notes—perfect for scanning options and choosing the best how-to. Use it to kickstart research and structure your interview plan.
7) SEO keyword analyzer. “SEO is dead. People don’t use search engines. Now they just ask LLMs.” But LLMs still use search engines—so SEO is not dead. I still care about ranking for relevant terms, and I use ChatGPT to help. I give it a target keyword and one of my articles, then ask it to analyze the top ten Google results and highlight what they do that I don’t. I get a prioritized gap analysis. I don’t take every suggestion—I write for humans first—but many SEO improvements also boost readability, so it’s a win-win. This workflow, also inspired by Andy Crestodina, made me care about SEO because the effort is now minimal. Skills I built: competitive research and gap analysis, balancing SEO with human readability, and codifying a repeatable research pattern.
8) Landing page analyzer. I don’t love writing sales copy, but landing pages matter. I use ChatGPT to critique my course landing pages, with rich context: an ideal customer profile from real discovery interviews, a course syllabus, student testimonials, and the same knowledge base my support team uses. With all that context, I ask for a critique from the buyer’s point of view. Context is king—the more I provide, the sharper the feedback. I don’t accept every suggestion, and I still run demand and usability tests, but a second set of (virtual) eyes helps me move faster on a task I’d otherwise procrastinate. Skills I built: using LLMs to push through resistance, feeding the right context, and soliciting targeted “expert” feedback.
Messaging teardown in a sleek, dark theme shows how to turn interview findings into sharper copy: center ICP struggles with adoption and scaling, and rework the hero to speak directly to product leaders under pressure.
9) Podcast participation guide. I launched a new podcast, Just Now Possible, where I interview product teams about the AI products and features they’re building. Guests often need company approval to join, and I’d never had to ask for permission before. I set up a ChatGPT Project with background files—target listener, goals, and differentiation strategy—then asked it to draft a one-pager for executives explaining why their team should participate. It nailed the brief because the Project was already loaded with the right context. Skills I built: setting up Projects for ongoing domains and compounding context over time for higher-quality assistance.
10) Podcast episode titles, descriptions, show notes, and chapter marks. In the same Project, I paste episode transcripts and ask for titles, descriptions, show notes, and chapters. As volume grows, I’m transitioning this into a CustomGPT with actions so I can click “Generate episode metadata,” paste the transcript, and go. Later, I’ll add actions for social posts and more. I don’t need to design the full system upfront; I evolve it as needs emerge. Skills I built: when to move from Projects to CustomGPTs, how to define actions, and how to evolve LLM tools incrementally.
Explore how the Just Now Possible podcast turns real AI product work into practical guidance. This overview invites PMs, designers, and engineers to share decisions, showcase features, strengthen employer brand, and gain recruiting assets.
Researcher: If you’ve tried using LLMs as an expert researcher at home, the returns at work are even better. Here are two recent examples.
11) Choosing a new blogging/newsletter platform. After 14 years on WordPress, my site started breaking—plugin auto-updates caused critical errors, Google flagged 500s and performance issues, and I was over managing plugins. I’d also switched from Mailchimp to Kit and wasn’t thrilled. I considered Substack but had mixed feelings. I laid out constraints and goals in ChatGPT, compared options, and landed on Ghost. Before committing, I used ChatGPT to dive deep: theme customization, memberships, API documentation, and migration tasks. On a free trial, ChatGPT walked me through exporting from WordPress and importing into Ghost; Claude Code helped with theme tweaks. By the end of two weeks, I had imported data, customized the site, validated fit, and built confidence. We officially migrated in August 2025. Skills I built: tackling big projects with an AI guide on call, running structured vendor comparisons, and piloting major tech decisions with AI-assisted validation.
A draft episode description in dark mode outlines a talk on creating an AI Teacher Assistant for K–5 schools—covering post‑COVID pressures, why a chatbot interface failed, building a first RAG system, and lessons from real teacher use.
12) Academic research. I draw heavily from research on decision-making, problem-solving, and learning science, but I’m not an academic and can’t spend hours in journals. ChatGPT’s Deep Research changed that. Quarterly, I generate a report on topics like decision-making with parameters such as date ranges, peer-reviewed sources, and clear citations. I automated the pipeline so reports land in my Readwise inbox alongside other articles. I also seeded a course design Project in ChatGPT with Deep Research reports on scaffolding, modeling, and learning styles, so my course design support is evidence-based by default. Skills I built: running Deep Research on-demand and automating it so staying current is effortless.
Learning to use AI as a thought partner has been the biggest unlock for me. It’s hard to describe, so I’ll show you with detailed examples. I’ll start with how I write with AI—headline generation and copy editing—and quickly get to more advanced workflows. You’ll see how I set up subagents to review my writing from different perspectives, where I let LLMs draft versus where I insist on drafting myself, and why I now write in VS Code with Claude Code following along.
See how Ghost uses Handlebars to render posts and customize themes quickly. The screenshot highlights template helpers and a straightforward flow: download a theme, edit locally, upload in Ghost Admin, then activate.
These workflows helped me produce more, higher-quality content, and—unexpectedly—brought the joy back to writing.
I’ll also share how I use LLMs to help me code: how ChatGPT taught me to set up and use a Python Jupyter Notebook for eval data analysis, how I pair program with Claude Code, how I get Claude Code to generate high-quality unit and integration tests, and how I leveled up error handling with both Claude Code and ChatGPT. I have a light coding background; I couldn’t have done this without LLMs. Even if you don’t code today, there’s a lot here you can apply.
Evidence-backed scaffolding methods at a glance—gradual release, cognitive apprenticeship, task simplification, mentoring, and communities of practice—show how to teach AI skills, build confidence, and accelerate adoption at work.
As a reminder, those last two sections—my Writing Partner and Coding Partner playbooks—are for paid subscribers. I’ll also use comments to dig into your workflows. I hope you’ll join us.
I was initially reluctant to use LLMs as a writing partner. I’m not trying to outsource my thinking; writing is how I think. But staring at a blank page is real. I write, delete, and write again. The breakthrough was realizing the model doesn’t have to think for me—it can help me think more clearly. It can tell me when a draft is weak, offer structured feedback, and help me brainstorm ways to get unstuck. That’s how I began using LLMs as a true thought partner.
