Why AI Sounds Right But Fails in Practice (A Podcast Editing Case Study)

LLMs are designed to give you an answer. They never say "I don't know." If you've prompted an LLM accordingly and you're lucky, it might ask clarifying questions before responding. But most of the time, it will give you an answer and you won't know if the answer is going to be right or wrong. Much like a manipulative salesperson it will sound convincing, give you good explanations and reasonings, it will likely make you feel good, activate your emotions but in the end it can still be very, very wrong.

I learned this while testing AI for podcast editing, specifically, for the task of creating the intros that should hook viewers in the first 30-80 seconds.

I've been editing podcasts for clients for years. The one I'm going to talk about now is a straightforward conversation podcast between experts. So, I've always done the intro work manually: watch the full podcast at 2x speed, mark interesting moments as I go, then edit them together into a compelling hook. The entire edit and processing of files takes about 1.5 to 2 hours, with the intro itself taking roughly half that time.

I thought AI could help me identify those compelling moments faster, maybe even catch things I missed while watching on fast-forward. And of course I wanted to test the technology. I've been seeing some people using totally automated workflows for their podcasts, and I wanted to know what was really possible. Could it actually save time and produce better results?

So far I tried it twice. The first time worked reasonably well: the AI gave me a solid shotlist, I adapted it significantly based on my own judgment, and the client was happy. The second time, I delivered what the AI suggested with some adaptations, and the client rejected it completely. It was actually the first time he'd asked for a complete redo on this project.

The AI had given me something that sounded right in theory but fell apart in practice.

The Experiment

My approach was straightforward: I exported a transcript of the podcast, fed it to the LLM (I've been experimenting with both ChatGPT and Claude), and asked the LLM to create a shotlist for the intro. I gave it examples of how other channels approach intros. It fed back to me what it understood those channels were doing to create good hooks. I gave an overview of the target audience and told it to look for moments that would engage them emotionally.

The first time I tried this, it gave me suggestions that seemed promising. But when I reviewed them, I saw they needed adaptation. The AI had identified some good moments, but it had also fabricated quotes - melting together words from the transcript into sentences that were never actually said. I reworked the edit based on the shotlist so it would flow properly, keeping maybe 70%, maybe 80% at the most, of the structure it suggested and rebuilding the sequence to make sense. The client was happy with the result, but honestly, it took me about as much time as doing it manually.

Still, I thought maybe I just needed to refine my prompts and process. So when I came across a tricky podcast, I tried again.

What Went Wrong

The second podcast I tried this method with was more technical. Not many obvious emotional peaks to hook viewers with.

I prompted the LLM carefully, explained the challenge, asked it to identify moments that would create interest despite the technical nature of the content. It gave me a section where it claimed there would be emotional engagement that would interest viewers. The explanation for why this would work sounded completely reasonable. It talked about curiosity gaps and pain points. It would be a moment that the audience could relate to.

When I looked at the suggested sequence I was surprised. It picked a lengthy example the guest gave and suggested chopping it up so it would fit into the intro. I hadn't considered using this example when I first reviewed the podcast because it seemed convoluted to me, but I wanted to give the LLM's suggestion a good chance.

Again, the LLM fabricated some quotes, stringing together words from different parts of the transcript into sentences that didn't actually exist. I had to verify everything and make the sequence work - I couldn't have just used the shotlist as it was. But I was intrigued and wanted to see if the overall approach would work. It was something I wouldn't have come up with myself. I wanted to know if the LLM could actually teach me something here.

But the client's response was clear: it didn't work. The sequence was too fragmented for someone who hadn't already heard the full podcast. He gave me simple instructions: use three specific clips from the first third of the podcast, about 30 seconds total, and keep it straightforward.

I followed his instructions and the result was clear and concise.

About the LLM's Confidence

Here's what made this experience valuable: the AI didn't just give me a bad suggestion. It gave me a thoroughly explained, confidently reasoned bad suggestion that almost convinced me to ignore my own instincts.

When the LLM suggested that fragmented sequence, it didn't say "I'm not sure this will work" or "this might be too complex." It explained very confidently why this approach would engage the audience. It sounded like someone who knew exactly what they were talking about.

But it was wrong.

I've noticed this pattern in other uses of AI as well. LLMs do well at creating something that looks good on the surface level, sounds good on the surface level, has the right rhythm and structure on the surface. But when you actually pay attention to the details and think about the audience experience, you find things that very clearly don't make sense. It might string together ideas that seem connected but don't actually build on each other logically. LLMs lack nuance and most of all actual, real understanding.

And that's the problem my client immediately identified when reviewing the work. A viewer needs clarity, not superficial value or fragments that mimic meaning.

This is the human judgment that AI can't replicate. It doesn't actually experience what it's creating. It can't feel whether something is too complicated or just complicated enough. It optimizes for what sounds good in theory, not for what actually works when a real person encounters it.

There are some automated tools and AI plugins that I use for editing. AutoPod Multi-Camera Editor, for instance, automatically cuts between speakers and different camera angles. I've used it in podcast editing for years now. It genuinely saves time and improves quality for this specific task. I still review the output to catch any obvious issues, but they're rare. The tool works because cutting between speakers is a mechanical task that follows clear rules: when person A speaks, show person A. When person B speaks, show person B. At the end of the day, it's just a podcast, it doesn't need dramatic editing. We're mostly listening to this thing, so functional works.

Asking AI to suggest intro structure is a little different. It requires understanding emotional flow, audience psychology, and contextual judgment about what will hook the audience. AI will give you confident answers about these things. It will explain its reasoning convincingly. But it doesn't actually understand what it's creating, and that's where it fails.

I also keep noticing that some podcasts now use totally automated workflows for everything: AI-selected clips, automated editing, no human review. It's impressive how much time they can save on processing. But when I've checked their YouTube performance, the numbers often aren't great. Low views, poor engagement. It makes me wonder if the efficiency is real, or if they're just efficiently producing content that nobody watches.

So where does this leave me with AI tools?

I haven't tried AI again for podcast intros, but I haven't ruled it out. If better prompting strategies or more suitable models emerge, I'll test them. My clients want efficient processes and quality results - both matter.

But I've learned to be skeptical of confident explanations that sound good in theory. The fundamental issue is this: LLMs sound convincing whether they're right or wrong.

For mechanical tasks that follow clear rules - like cutting between speakers - AI tools work well. I can verify the output immediately and the rules are consistent.

For creative judgment - understanding what will hook an audience, what feels too complex, what actually works in practice - that still requires human experience. Not just the technical skill, but the instinct that comes from actually caring about whether the work connects with people.

When I trusted the LLM over my own instincts, I disappointed my client's expectations. When I listened to that instinct that something felt off, even though the AI's explanation was compelling, I was right.

That instinct is what makes creative work valuable. It's developed through experience, through failures, through paying attention to what actually works rather than what sounds good in theory. AI can analyze patterns, but it can't develop that instinct.

The tools will keep improving. I'll keep testing them. But I won't mistake a convincing explanation for genuine understanding. That distinction matters - not just for the quality of my work, but for why the work matters in the first place.