Using LLMs in Real Projects

admin | October 29, 2025

AI & Machine Learning Productivity Project Management Tech How-Tos

How I Actually Use LLMs in Real Projects (And What I’ve Learned)

If you’ve spent any time around tech blogs or LinkedIn lately, you’ve probably seen a lot of talk about Large Language Models—yep, those fancy AIs everyone calls LLMs. But let’s be honest: it’s one thing to read the hype and another to actually use LLMs in real projects. So today, I want to get real about how I’ve used LLMs in my own work, what’s surprised me, and what I wish I’d known before diving in.

What Even Is an LLM (And Why Should You Care)?

Let’s break it down. LLM stands for Large Language Model, which basically means a super-smart AI trained on a massive amount of text. Think of them as the brains behind tools like ChatGPT or Google Bard. They can write emails, help with coding, summarize documents, or even generate poetry (if you’re into that).

But here’s the real question: can they actually help with your (or my) real-life projects? Spoiler: Yes, but there’s a learning curve.

How I’m Actually Using LLMs in My Workflow

Drafting Content: Sometimes I’ll use an LLM to spit out a rough draft of a blog post or email. It’s not always perfect, but it gets me over the “blank page” anxiety way faster.
Code Help: I’m not ashamed to admit I ask AI to help me debug code or write boilerplate snippets. It’s like having a patient junior developer who doesn’t mind silly questions.
Summarizing Stuff: When I’m drowning in meeting notes or research papers, I’ll paste chunks into an LLM to get the gist. Not 100% accurate, but a huge time-saver.
Brainstorming & Outlining: I’ll throw a prompt at an LLM to get ideas flowing—sometimes it gives me gold, sometimes it’s a little weird, but that’s half the fun.

What’s Not So Perfect (Yet)?

Look, I love LLMs, but they’re not magic. Sometimes the answers are way off, or the tone is just… odd. You’ve got to fact-check and tweak. Also: privacy is a real thing to consider. I try not to paste sensitive info into these tools, because, well, you never really know where your data ends up.

And don’t get me started on hallucinations (that’s AI-speak for “making stuff up”). I’ve seen some very confident but totally wrong answers. So yeah, double-check before you copy-paste.

My Hard-Earned Tips for Using LLMs in Projects

Start Small: Don’t throw your most critical project at an LLM on day one. Try it out on low-stakes tasks first.
Be Specific: The clearer your prompt, the better the response. Vague requests get vague answers—ask me how I know!
Edit and Review: Always check the AI’s output. It’s helpful, but it’s not your editor-in-chief.
Think About Privacy: Don’t share anything confidential. Use anonymous or generic data when possible.
Have Fun With It: Half the joy is seeing what the AI comes up with—sometimes it’s brilliant, sometimes it’s a head-scratcher.

So, is using LLMs in real projects worth it? For me, it’s a big yes—but only if you go in with realistic expectations and a willingness to experiment. If you’re already using LLMs, I’d love to hear your best tips (or funniest fails) in the comments. And if you’re on the fence, maybe try it for your next small project—you might be surprised!

Got questions, experiences, or wild LLM stories? Drop them below—I read every comment!