I didn’t expect that tinkering with a small writing tool would teach me so much about coding and communication. My goal was simple—help people rewrite sentences more clearly—but I quickly ran into challenges I hadn’t anticipated.
People type messy stuff. Some sentences are half-formed, some mix languages, and some have typos that completely change the meaning. To deal with this, I built a small text processing pipeline, which I tested in Sentence Rewriter. Running real sentences through it helped me see which fixes actually mattered. The main steps looked like this:
Language detection: figuring out what language the input was in, so the rest of the pipeline could handle it properly.
Cleaning and normalizing text: removing extra spaces, fixing punctuation, and standardizing characters.
Basic spelling and grammar correction: fixing obvious mistakes so the AI could understand the sentence better.
Handling emojis or unusual symbols: replacing them with placeholders or simple text descriptions to avoid confusing the AI.
Even after preprocessing, the AI could still be inconsistent. I spent a lot of time tweaking prompts, testing small wording changes, specifying input and output formats, and adding examples. Running these experiments in real time through Sentence Rewriter showed me immediately what worked. It was a surprise how much prompt wording could change the results—writing prompts felt a lot like writing documentation or code comments: small details matter.
Some tricky sentences still failed, so I added logging to capture input, output, and the intermediate steps. I also kept a few edge-case tests—nested clauses, slang, odd punctuation—to see patterns in what broke and why. Going through these logs forced me to think carefully and document the reasoning behind each step. Debugging became less about random fixes and more about understanding the process.
By the end, I realized this project wasn’t just about building a tool. It became a series of mini experiments that helped me think more clearly and explain ideas better. Every messy sentence I processed, every prompt I refined, every log I reviewed—it all added up. Even small projects like this can teach lessons that go far beyond the code.
Top comments (1)
It's crazy how much complexity you can find in a system when you're really trying to do it right, huh?
Reading through your preprocessing pipeline made me wonder—have you experimented with surfacing some of these steps to users? Like, when the language detection is uncertain, or when there are multiple ways to interpret a messy sentence, letting the AI ask clarifying questions instead of guessing? I'm curious if making some of that invisible work visible might actually help users understand why rewrites work the way they do.