Starting with raw audio transcripts
The first time I dropped an hour long transcript into ChatGPT it was a total mess. I copied the whole thing from Otter and pasted it in, and what came out on the other side was basically the transcript with slightly cleaner punctuation. No structure. No personality. Definitely not a blog post. The problem is that a transcript is like a raw dump — too many filler words, random pauses, and off topic tangents. If you just tell ChatGPT “make this into a blog,” it usually panics somewhere in the middle and spits back something robotic. What I learned was I had to guide it step by step, almost like I was teaching a friend how I wanted the story retold.
So before I even think about prompts, I do lightweight prep. I’ll scan the transcript looking for obvious junk — “uh,” “you know,” and the times someone forgot what they were saying halfway through. I don’t delete every filler word, but cutting a good chunk before sending it into ChatGPT makes the output less bloated. Once, I skipped this step and pasted a full three person podcast transcript, and the output literally cut off mid sentence because it hit the character limit. At that point I learned the hard way to slice these things into sections first.
Breaking transcripts into chunks that make sense
Another rookie mistake is pasting everything at once. Most platforms cap the input size and then you spend twenty minutes wondering why your text stopped right in the middle of a sentence. I got smarter about this by breaking the transcript by topic instead of random length. For example, if the speaker starts telling a customer support story, I highlight that as its own section and use that as one block. Doing it this way keeps the blog post much more coherent. If you just chop by word count, you end up splitting a thought right before the punchline, and then ChatGPT starts the next output as if it forgot what conversation we were having.
One trick I use in the prompt is saying something like: “Pretend this chunk is its own mini essay. Rephrase it into clear paragraphs as if you were explaining it casually for a blog.” That way, when I stitch the chunks back together later, they already sound self contained and natural. Otherwise I had cases where the second chunk started with “As I was saying earlier” — and of course, earlier had been deleted because it was another chunk.
Crafting prompts that actually work
The big lesson I learned is the vaguer you are, the more generic the answer. If you just tell ChatGPT “make this into a blog post,” what comes out reads like a technical manual. I’ve found that giving style directions works better. For instance, I once said: “Rewrite this as a casual blog where the writer is venting about their tech issues, keep it conversational, and use a couple of text emoticons like 🙂 or :P.” That got me way closer to the tone I actually use. If I leave that out, it defaults to textbook mode.
Another thing that saved me is explicitly telling it what not to do. For example, I say: “Don’t summarize, keep the speaker’s details, and do not create numbered lists.” Because otherwise you’ll end up with bullet point breakdowns that sound like project documentation. The more you act like a picky editor, the more the output feels usable. Oh, and I also ask it to “speak like a friend explaining things to a beginner.” Without that, I get readouts that could belong in a research journal.
Forcing it to keep the real details
One huge problem is ChatGPT has this urge to simplify — almost too much. I once gave it a story where the person explained the exact Zapier trigger they used, and it threw out the detail and said something bland like “they automated some steps.” That’s useless. So I learned to push back in the prompt with “retain exact actions and examples, do not generalize them.” That way, instead of “they automated notifications,” it will actually say, “they made a Zap in Zapier that took every new Google Form entry and posted it into a Slack channel.” Beginners understand that way better because they can imagine clicking those buttons.
Sometimes it still forgets and trims details. When that happens, I copy paste the original transcript chunk and literally bold the parts I want to keep, then say: “Your rewrite must include all bolded items.” It feels silly, but it works. When it includes the nuts and bolts, the post actually feels human and helpful instead of fluffy filler.
Cleaning up the flow without losing honesty
The really delicate part is editing without losing the rawness of someone talking. If you over polish, the post reads fake. I aim for messy but readable. Like, I’ll let sentences be a little run on in the rewrite because that feels authentic. Real humans don’t always speak in perfect three sentence paragraphs. I ask ChatGPT: “keep conversational pauses but remove duplicated filler.” That usually gives me something like, “I tried it twice, but then… yeah, the app froze.” Which sounds true to life, instead of: “The application failed.” The second version is accurate but dead.
One time it even improved the joke. The speaker had said something like “Yeah, my screen froze, and I guess the app got tired of me.” ChatGPT rewrote it as “My screen froze, almost like the app decided to take a nap.” 🙂 That kind of light touch is perfect. The blog still sounds natural without being robotic.
Mixing in structure after the rewrite
When I have all the cleaned up text, I’ll finally start molding it into blog shape. Not before, because structuring early just gets ruined once ChatGPT does its rewrite. At this stage, I pull together the segments and use headings to break up topics. If the transcript covers three parts — like backstory, the issue, and the fix — I give each of those a heading. Sometimes I even throw in a little ASCII style table like:
“`
Transcript phrase | Blog rewrite
—————— | ——————————-
“uh this thing uhh”| “this feature confused me at first”
“wait hold up” | “I stopped because something broke”
“`
That way, beginners can see how speech transforms into text. It feels more like teaching than dumping a polished essay on them.
Reusing prompts for predictable results
After wasting too much time guessing, I built a small library of prompts that I keep in Notion. Stuff like: “Rewrite as casual blog, include speaker detail, preserve mistakes that matter, make it simple enough for a beginner to grasp.” When I paste that every time, I get consistent results. When I wing it, I sometimes get ultra formal text that I have to throw away.
I also learned the hard way not to include too many instructions at once. I once gave ChatGPT a prompt that was like five paragraphs long, and it just ignored half of my requests. It’s better to keep it short and clear, and if it messes up, follow up with a second instruction. That’s how I end up with cleaner, more usable blogs without rewriting everything manually.
When things still go sideways
Even with practice, sometimes the output still breaks. I’ve had edits where the rewrite repeated the same story twice. I’ve had situations where it forgot entire paragraphs. My fix is usually going chunk by chunk — feeding the original line back in and saying “Rewrite only this paragraph.” It’s slower, but it guarantees I don’t lose details. And honestly, sometimes I just laugh when it goes wrong. Like the time it rewrote a customer rant and somehow made it sound like an academic thesis. ¯\\_(ツ)_/¯
Anyway, no matter how you spin it, the core is you can’t trust it to guess your style. You have to babysit the prompts, guide it with real examples, and demand the details stay intact. Otherwise you get generic fluff that nobody wants to read.