5 - Slushie Help from AI

9 - I Asked AI: Am I Going to Be Okay in Retirement?"

I asked AI a question I'd been carrying around for years: am I going to be okay in retirement? Not a generic question — I gave it real scenarios, honest fears, and a budget. What I got back wasn't a magic answer. It was something more useful: clarity. The Question Behind the Question I've done the responsible things. Consistent 401k contributions, a Roth IRA, emergency savings, and now a job that could give me a pension if I stay long enough. But I started late. And for all the calculators I've run and conversations with my financial advisor, the fog never fully lifted. I still didn't really understand what my retirement looked like. The fear underneath all of it: what happens if I don't make the full 10 years? What if there are layoffs? What if my health doesn't cooperate? I wasn't looking for the best-case scenario. I wanted to understand the realistic range — and what the tradeoffs actually are. How I Prompted It I didn't just ask "help me with retirement." I gave it real context: the accounts I have, my approximate balances (in round numbers — more on that in a minute), my timeframe, and four specific scenarios. Early exit with no pension. Making five years and getting a partial pension. Making the full 10 years. And then layered on top of each: what if I take Social Security early versus waiting until 70? I also told it something important: money makes me panic. I can do math fine, but the emotion of money shuts me down. I asked it to explain things in a way that would actually make sense to me — not just run numbers, but help me understand what I was looking at. What It Showed Me: Guaranteed vs. Flexible Income The most useful framing AI gave me was the split between guaranteed income — pension, Social Security — and flexible income that depends on the market. In the full 10-year scenario, enough of my income is guaranteed that market swings matter less. In the early exit scenario, I'm much more exposed to market fluctuations. That gap became very visible, and it was less scary than I expected, but it was real. The middle scenario — making five years but not ten — was the one that surprised me most. It's not as protected as I thought. I'd be more at the mercy of market conditions than I'd realized. Seeing all three paths side by side made the tradeoffs concrete in a way that a single number never had. The Social Security Timing Question This is one where people have strong opinions, and AI helped me think through the actual math for my situation. Waiting until 70 means a significantly higher monthly payment. Taking it earlier means more months of income during the gap years before 70. The break-even in my scenario came around age 80 — meaning if I live past 80, waiting pays off. If I don't, earlier is better. But it's not just math. It's health, cash flow, and what you're actually going to need at different points in your life. At 70, I could probably still pick up part-time work if I needed to. At 80, maybe not. That reframe — thinking about when you're most financially vulnerable, not just when the math optimizes — changed how I was looking at it. The Budget Reframe That Surprised Me Most I had been comparing my retirement budget to my current budget and panicking at the gap. AI helped me stop doing that. In retirement, payroll taxes disappear. Retirement contributions stop. Work-related expenses go away. When you strip all of that out, the actual cost of living in retirement is significantly lower than what I'm spending now. The gap I was afraid of is a lot smaller than I thought. What AI Can't Do — and What It's Great At AI is not a financial advisor. It doesn't know your exact numbers unless you give them, and you should be thoughtful about what you share — I used round numbers and kept things general rather than giving actual balances. It can't know your real pension formula, your exact tax situation, or account for everything a licensed professional would. Those decisions still belong with your financial advisor. What it is excellent at is holding complexity without getting overwhelmed, and explaining it back to you in a way that makes sense. I went to bed, woke up the next morning with more questions, and picked the conversation back up. By the time I met with my actual financial advisor, I had specific questions instead of general anxiety. That meeting was completely different. From Worrying to Understanding The shift I'm describing isn't from uncertainty to certainty — nothing about retirement is certain. The market will do what it does. Social Security may or may not look the same. Health is unpredictable. But I moved from a fog of general worry to a clear picture of the paths in front of me and what each one actually means. That's what I want for you. Stop asking whether you're going to be okay, and start asking what the map looks like. What's guaranteed and what's flexible? How exposed are you to market swings in each scenario? What does your actual budget look like — not compared to now, but compared to what retirement actually costs? When you can see those things side by side, you're doing retirement understanding, not just retirement planning. Next episode: the part two of this conversation — the heart side of the retirement question, not just the numbers. Jill’s Links http://jillfromthenorthwoods.com [https://abetterlifeinsmallsteps.com/] https://www.youtube.com/@startwithsmallsteps [https://www.youtube.com/@startwithsmallsteps] https://www.buymeacoffee.com/startwithsmallsteps [https://www.buymeacoffee.com/startwithsmallsteps] https://twitter.com/schmern [https://twitter.com/schmern] Email the podcast at jill@startwithsmallsteps.com [jill@startwithsmallsteps.com] By choosing to watch this video or listen to this podcast, you acknowledge that you are doing so of your own free will. The content shared here reflects personal experiences and opinions and is intended for informational and educational purposes only. I am not a software developer, data scientist, or AI professional. Any tips, tools, or suggestions offered should not be considered a substitute for professional technical advice. AI tools and platforms change frequently — always verify current features, pricing, and terms directly with the providers. You are solely responsible for any decisions or actions you take based on this content.

8 - Ask AI the Questions You’ve Pondered for Years

Someone said to me recently: you have the most interesting conversations with AI. And I thought about it, and I think they’re right. I have questions I’ve been carrying for years — things that felt too small to research, too specific to Google, too embarrassing to ask out loud. The recipe that never quite worked. The winter I remembered as a kid that was somehow worse than all the others. A Duran Duran song that made absolutely no sense. What my grandmother’s early life actually looked like. For years these just lived in the back of my head, filed under mystery — no resolution possible. AI changed that. You Don’t Need a Polished Prompt One of the most freeing things I’ve learned about working with AI is that you don’t need a formula. You don’t need to have researched your question first or know how to frame it perfectly. You can just ask. The question doesn’t need to be impressive. It just needs to be honest. AI is infinitely patient, it doesn’t make you feel dumb, and it can go as deep or as surface-level as you want on anything from serious research to wildly random curiosity. Real Questions, Real Answers: What This Actually Looks Like The recipe question: I’d had a steel-cut oat and split yellow lentil recipe for years — healthier, higher protein, you don’t taste the lentils — but it was always slightly off and I could never figure out why. I told AI what I was making, what device I was using (a Ninja Foodi pressure cooker), and what kept going wrong. It identified the problem: my water ratios were off, and I didn’t fully understand how pressure cooking changes the process compared to an open fire. It gave me a corrected recipe card, troubleshooting steps, and versions adapted for a stovetop pot and slow cooker. It also taught me how to use my own machine better. The 1978 winter: I grew up in the Midwest and remembered one winter as being dramatically worse than anything around it. I wanted to know why. AI explained the strong La Niña pattern that year and the series of intense storm systems that stacked on top of each other. It confirmed that my childhood memory wasn’t just a feeling — it was a genuinely historic winter. Sometimes AI isn’t about learning something new. It’s about finally having confirmed something you half-remembered for decades. The grandmother question: My grandmother was born in Lithuania, escaped with her mother to Tel Aviv when it was part of the Turkish Ottoman Empire, and eventually came to America through Ellis Island. I knew she left after her father died — but AI filled in something I hadn’t known: families like hers were being expelled by the Turkish government at that time. Suddenly I understood the context of her life in a way I never had. A chance to learn what I wished I’d asked her while I still could. The Questions Nobody Taught You to Ask Some of what I bring to AI isn’t curiosity — it’s the kind of practical knowledge that most people get from their parents and I had to piece together on my own. How often should you wash sheets? What’s the standard way to greet someone you don’t know? How do you store food containers so the lids actually stay with the right item? These feel silly to say out loud. AI doesn’t think they’re silly. It just answers. And then there are the rabbit holes: why does the sky turn green before a tornado? What did a specific Duran Duran lyric actually mean? I once described a song I’d heard in a bar — I knew the band, roughly the year, and that it changed tempos in a strange way — and AI identified it from that description alone. What This Is Really About The point isn’t any single question. The point is that most of us are walking around with more curiosity than we’ve ever had an outlet for. Things we wondered as kids. Things we assumed were wrong that turned out to be right. Things we assumed were right that turned out to be wrong. Things we were too embarrassed to ask anyone. AI is patient, specific, non-judgmental, and available at eleven o’clock at night when you’re standing in your kitchen staring at a pot that still doesn’t taste right. You don’t need a formula. Just ask. Jill’s Links http://jillfromthenorthwoods.com [https://abetterlifeinsmallsteps.com/] https://www.youtube.com/@startwithsmallsteps [https://www.youtube.com/@startwithsmallsteps] https://www.buymeacoffee.com/startwithsmallsteps [https://www.buymeacoffee.com/startwithsmallsteps] https://twitter.com/schmern [https://twitter.com/schmern] Email the podcast at jill@startwithsmallsteps.com [jill@startwithsmallsteps.com] By choosing to watch this video or listen to this podcast, you acknowledge that you are doing so of your own free will. The content shared here reflects personal experiences and opinions and is intended for informational and educational purposes only. I am not a software developer, data scientist, or AI professional. Any tips, tools, or suggestions offered should not be considered a substitute for professional technical advice. AI tools and platforms change frequently — always verify current features, pricing, and terms directly with the providers. You are solely responsible for any decisions or actions you take based on this content.

7 - You’ve Been Using AI for 20 Years and Didn’t Know It

You didn’t miss the AI revolution. You’ve been living in it for decades — you just didn’t have a name for it. In this episode of Small Steps with AI, we trace the AI you’ve already been using your whole life: the recommendation engines, the spam filters, the fraud alerts, the predictive text — and explain what actually changed when AI learned to have a conversation. This episode covers how AI has been quietly personalizing your world since long before ChatGPT, why you were training these systems even as they were shaping you, what the old narrow AI couldn’t do no matter how smart it was, and what genuinely shifted when conversational AI arrived. Plus a personal story about how writing has always been hard — and why that changed. If you’ve felt like you’re late to AI or not sure where to start, this episode is your entry point. Question for you: What’s one thing in your life that background AI has already made easier — even before you thought of it as AI? Jill’s Links http://jillfromthenorthwoods.com [https://abetterlifeinsmallsteps.com/] https://www.youtube.com/@startwithsmallsteps [https://www.youtube.com/@startwithsmallsteps] https://www.buymeacoffee.com/startwithsmallsteps [https://www.buymeacoffee.com/startwithsmallsteps] https://twitter.com/schmern [https://twitter.com/schmern] Email the podcast at jill@startwithsmallsteps.com [jill@startwithsmallsteps.com] By choosing to watch this video or listen to this podcast, you acknowledge that you are doing so of your own free will. The content shared here reflects personal experiences and opinions and is intended for informational and educational purposes only. I am not a software developer, data scientist, or AI professional. Any tips, tools, or suggestions offered should not be considered a substitute for professional technical advice. AI tools and platforms change frequently — always verify current features, pricing, and terms directly with the providers. You are solely responsible for any decisions or actions you take based on this content.

6 - Using AI as a Writing Partner — When to Use It, When Not To

I have written emails at two in the morning that I never sent. I have drafted letters in my head during a long drive and then sat down at my keyboard and couldn't get a single sentence right. Writing has always been the place where I get stuck — not because I don't know what I want to say, but because I can't seem to say it the right way. Too much Jill in it. Too much heat, too much history, too much something. What I didn't know until recently is that AI would become the writing partner I never knew I needed — and it changed the way I think about what writing is even for. The Core Insight: Sometimes the Best Letter Sounds Like No One in Particular Here's what I discovered: sometimes the best version of what needs to be said is calm, clear, and professional — without your personality all over it. AI does this naturally. It has no history with the person you're writing to. It has no frustration, no backstory, no emotional residue. For certain situations, that's not a limitation. It's exactly what the letter needs. Example 1: The Resume When I applied for a new job after fifteen years of not needing a resume, mine was three pages long, badly organized, and full of redundant language. I didn't ask AI to rewrite it — I asked it to reorganize it. Here's what I have; put related things together, cut redundant language, don't invent anything. It came back a page and a half. Better organized than I could have done it. Same facts, much cleaner presentation. And it worked. Example 2: The Board Member Email I had to write an email to a fellow board member — someone who reads confrontation into innocuous sentences and tends to respond with real heat. The situation needed to be addressed. But if there was any warmth in the writing, any frustration, any hint of me, it would make things worse. So I told AI the situation, the relationship, the goal. I asked for something neutral, measured, professional, non-confrontational. What it came back with was a little formal, a little robotic — and exactly right. The email worked. The situation got handled. Example 3: The Resignation Letter Leaving a job I'd been at for fifteen years was emotionally complicated. But the letter didn't need to go to my boss — it needed to go to HR, in a city I'd never been to, to a person I'd never met. The letter needed to be dignified, professional, and blank. Dates, gratitude for the opportunities, acknowledgment of my supervisor. Nothing embarrassing. AI gave me exactly that in about thirty seconds. Something that would have taken me an hour to write and still might not have been right. When Not to Use AI for Writing The closer the relationship and the more the letter is about that relationship, the more it has to come from you. A message to a friend who is grieving. A thank-you note to someone who went out of their way for you. A letter to your child. Those need to sound like you — and increasingly, people can tell when they don't. AI writing has a certain evenness to it, a smoothness, that can feel distant when warmth is what the person needs. The rule I've landed on: the more professional and situational, the more AI can help; the more personal and relational, the more it needs to be you. How to Check Your Output Before you send anything AI helped you write, ask yourself: does this sound like a real human wrote it, or does it sound like AI? If something feels stiff, tell AI. "The third paragraph is a little formal — can you make it sound more natural without losing the professional tone?" Ask AI to critique its own output. Surprisingly, it's quite good at this. The back-and-forth is where the best drafts come from. The Key to Better Output: Context Is Everything A vague prompt gets you a vague email. If you tell AI who you're writing to, what the relationship is, what happened, what you're trying to achieve, and how you want to sound — you'll get something you might actually want to send. The more specific you are upfront, the less revision you'll need. Think of it as briefing a very competent but very literal assistant who knows nothing about your situation unless you tell them. Next episode we'll look at using AI for billing disputes, insurance letters, and correspondence where you need something very specific said without needing a lawyer. Thanks for being here. Jill’s Links http://jillfromthenorthwoods.com [https://abetterlifeinsmallsteps.com/] https://www.youtube.com/@startwithsmallsteps [https://www.youtube.com/@startwithsmallsteps] https://www.buymeacoffee.com/startwithsmallsteps [https://www.buymeacoffee.com/startwithsmallsteps] https://twitter.com/schmern [https://twitter.com/schmern] Email the podcast at jill@startwithsmallsteps.com [jill@startwithsmallsteps.com] By choosing to watch this video or listen to this podcast, you acknowledge that you are doing so of your own free will. The content shared here reflects personal experiences and opinions and is intended for informational and educational purposes only. I am not a software developer, data scientist, or AI professional. Any tips, tools, or suggestions offered should not be considered a substitute for professional technical advice. AI tools and platforms change frequently — always verify current features, pricing, and terms directly with the providers. You are solely responsible for any decisions or actions you take based on this content.

5 - Slushie Help from AI

I bought a refurbished slush machine. No instructions. And what happened next turned into one of the best examples I’ve had of what AI actually is — not a magic answer machine, but a thinking partner who helps you work a problem all the way to a real conclusion. This episode is the full story: the curiosity, the chemistry lesson, the recipes, the failures, the controlled test, and the moment I finally knew the machine was the problem and not me. THE CURIOSITY THAT STARTED IT It didn’t start with frustration — it started with a question: what can this thing actually do? That shift from “what does the box say” to “what could I do with this” is one of the most useful things you can bring to a conversation with AI. I wasn’t looking for a recipe. I was exploring a possibility. LEARNING THE CHEMISTRY (THE PART I DIDN’T EXPECT) Slush machines aren’t as simple as they look. To work correctly, the liquid needs the right amount of dissolved solids — what’s sometimes called “sugar behavior” — to stay slushy instead of freezing solid. AI walked me through why that matters and what ingredients — real fruit, dairy, small amounts of sugar, or alternatives like Allulose — could satisfy that requirement while still fitting my health goals. BUILDING REAL RECIPES (NOT JUST IDEAS) From that understanding, we built actual recipes. Coffee-based slushes. Berry blends with frozen fruit and yogurt. Lighter drinks using fruit powders and structure. We talked about fat for mouthfeel, a pinch of salt to lift flavor, and texture stabilizers. By the end, I wasn’t just holding a list — I understood why each ingredient was there. WHEN THE MACHINE DIDN’T WORK I tried everything. Adjusted sugar levels, chilled the liquid, simplified the recipes, changed the settings. Every time: run, beep, stop. No slush. My first instinct was to blame myself. That’s worth noticing, because it’s a very human default. But instead of spiraling, I kept troubleshooting systematically — because that’s what AI had helped me set up. THE CONTROLLED TEST THAT SETTLED IT The most important advice in this whole story: run a definitive test. Not another creative variation — a controlled one. Cold apple juice. Nothing else. If the machine can’t slush that, the machine is the problem. I ran it. Same result. And that settled it. This was the most clarifying moment: sometimes the system you’re working with simply isn’t capable of the result you need, and the right move is to stop. THE BIGGER TAKEAWAY: MATCH YOUR GOAL TO YOUR TOOL After returning the machine, I stepped back and asked a better question: what was I actually trying to create? The answer had nothing to do with slush. It was about something that feels like a treat, fits into my life, and maybe supports my health. That reframe opened up better options — including machines built around frozen bases rather than sugar-heavy liquids. Sometimes you don’t need a better recipe. You need a better match. The small step here isn’t “try harder.” It’s test it, understand what the results are actually telling you, and then make a clear decision based on reality — not hope. AI can walk alongside every step of that process. That’s what it’s for. Jill’s Links http://jillfromthenorthwoods.com [https://abetterlifeinsmallsteps.com/] https://www.youtube.com/@startwithsmallsteps [https://www.youtube.com/@startwithsmallsteps] https://www.buymeacoffee.com/startwithsmallsteps [https://www.buymeacoffee.com/startwithsmallsteps] https://twitter.com/schmern [https://twitter.com/schmern] Email the podcast at jill@startwithsmallsteps.com [jill@startwithsmallsteps.com] By choosing to watch this video or listen to this podcast, you acknowledge that you are doing so of your own free will. The content shared here reflects personal experiences and opinions and is intended for informational and educational purposes only. I am not a software developer, data scientist, or AI professional. Any tips, tools, or suggestions offered should not be considered a substitute for professional technical advice. AI tools and platforms change frequently — always verify current features, pricing, and terms directly with the providers. You are solely responsible for any decisions or actions you take based on this content.