Last week, I was staring at my latest blog draft, wondering why some sentences just sounded… off. Even though I’d let the AI generate most of it, a few phrases still felt clunky. That’s when I got curious: could I make the AI check itself before I even looked at it?
So I started experimenting with self-correcting LLMs—essentially, letting the model generate a draft, detect likely mistakes, suggest fixes, and then pick the best version. At first, I thought it would be simple. Spoiler: it wasn’t.
The First Pass
I wrote a tiny loop in Python, just to see what would happen:
draft = model.generate(prompt)
for i in range(2):
errors = detect_errors(draft)
if errors:
candidates = generate_candidates(draft, errors)
draft = select_best_candidate(candidates)
The first pass was… messy. The AI caught some glaring mistakes but somehow mangled a few longer sentences even more. I leaned back in my chair and laughed. Technology is amazing, but sometimes it’s also stubborn.
Learning Curve
After a few tweaks—adding a simple grammar scoring function, flagging ambiguous pronouns, and checking subject-verb agreement—the second pass started to feel a lot smarter. I also ran some of the sentences through Grammar Checker just to compare the AI’s suggestions with a dedicated grammar tool. It was interesting to see where the model caught things the tool didn’t, and vice versa.
Interestingly, some things didn’t improve. Long dependencies, subtle style choices, and context-dependent phrases were still tricky. I jotted notes in my notebook: “Maybe combine heuristic rules + multi-pass LLM for better stability.”
Insights and Surprises
Here’s what surprised me the most:
- Self-correction feels a bit like having a junior editor who’s enthusiastic but inexperienced. It catches the obvious stuff, misses the subtle.
- Lightweight rule-based checks make a huge difference in stability.
- Watching the AI “debate with itself” over two passes is oddly satisfying.
To double-check my observations, I also ran parts of the draft through Grammar Checker again. Comparing the AI’s multi-pass corrections with the tool’s recommendations gave me a lot of insight into where LLMs excel and where they still need guidance.
By the end of the experiment, I had a draft that was noticeably cleaner, but more importantly, I had learned a lot about how models reason, what they struggle with, and how iterative feedback can help.
Final Thoughts
Playing with self-correcting LLMs reminded me that AI isn’t magic—it’s a partner. If you set up the right loops and add a bit of guidance, even a small model can produce surprisingly solid drafts. The blend of automation and human intuition is where the real fun happens.
Top comments (1)
good one