When Deterministic Text Transformation Beats AI

Look, I love AI writing tools. ChatGPT has saved me countless hours. But if you've spent any time using these tools to polish your writing, you know the frustration. Sometimes you get gold. Other times you get... well, something that kind of sounds like what you wanted, but not quite.

The core issue is that LLMs are unpredictable by design. They're probabilistic systems trying to guess what comes next. That's great for creative work, but when you just need to clean up a draft without it rewriting everything? Not so much.

This is why we built BotWash around deterministic text transformation instead.

Why LLMs Struggle with Editing

Here's the thing about using AI to edit your writing: you're basically rolling the dice every time.

The results change every time. Paste the same paragraph into ChatGPT twice, ask for the same improvements, and you'll get different results. Try maintaining a consistent brand voice across fifty blog posts when your editing tool produces random variations. Good luck.

Hallucinations sneak in. I've seen LLMs confidently add statistics that don't exist, swap technical terms for similar sounding wrong ones, and insert claims I never made. When you're editing a knowledge base article or academic paper, you can't afford that risk.

You never know why it changed things. An LLM rewrites your sentence, and sure, it looks better. But why did it do that? You can't learn from the edits, you can't replicate the logic elsewhere, and you definitely can't customize the behavior.

The costs add up. API calls aren't free, and they're not instant either. If you're processing lots of content or just want quick feedback while writing, those seconds of waiting and dollars per request start to hurt.

Prompt engineering becomes a second job. You find yourself writing longer prompts than the text you're editing: "Make this more professional but keep it casual, no jargon except these specific terms, keep sentences short but not too short, maintain my examples exactly..." And then it ignores half your instructions anyway because it's still just predicting tokens.

A Different Approach

What if instead of asking an AI to "make this better," you just told it exactly what to do?

That's deterministic text transformation. You define the rules:

• Strip out every instance of "very" or "really"
• Convert passive voice to active
• Split sentences over 25 words
• Swap "utilize" for "use"
• Fix heading capitalization

Same input, same output. Every single time. You can see exactly what changed. It runs instantly. No API costs. And you can tweak the rules to match your exact style.

How BotWash Works

The problem with rule-based transformation has always been that you need to code it yourself. Until now, anyway.

We built BotWash with over 100 pre-made text operations across 12 categories. Clarity improvements, tone adjustments, grammar fixes, SEO optimization, structure changes, basically everything you'd want to do to text. No coding required.

You combine these operations into "formulas" (we had to stick with the car wash theme). Think of it like building a pipeline:

1. Foam – Strip out filler words
2. Scrub – Fix grammar and typos
3. Rinse – Convert passive voice to active
4. Polish – Adjust the tone

Each step shows you exactly what it changed. You can see the before and after for every single operation. And it all runs in milliseconds.

When You Should Still Use LLMs

Don't get me wrong, LLMs are amazing for certain tasks. If you need to generate a first draft from scratch, summarize a 50 page document, translate between languages, or brainstorm creative ideas, use an LLM. They're built for that.

But for refining and polishing text you've already written? When you need the same edits applied consistently, when you want to know exactly what changed, when you can't afford hallucinations, and when you want results instantly?

That's where deterministic transformation shines.

How People Actually Use This

A few real examples from early users:

Documentation teams stopped fighting with ChatGPT to "make the docs clearer" and getting different results every time. Instead they built a formula that strips jargon, converts passive voice, and breaks up long sentences. Same polish, every doc, every time.

Marketing teams were tired of AI tools randomly shifting their brand voice. Now they have different formulas for different channels: one for professional emails, one for social media posts, one for blog content. The brand voice stays consistent because the rules are explicit.

Solo writers who were spending 20 minutes manually editing each draft now run a quick formula that catches their personal bad habits (overusing "actually," inconsistent heading caps, whatever). Takes 2 seconds instead of 20 minutes.

Give It a Try

If you want to test this out:

Start with someone else's formula from the community hub. Paste in your text and see what changes.

Then fork a formula and tweak it. Maybe you want different tone adjustments, or you have specific words you always want to replace. Make it yours.

Build up a library of formulas for different contexts: one for emails, one for documentation, one for blog posts. Unlike LLMs, they'll do exactly the same thing every time you use them.

Why This Matters

LLMs are fantastic for a lot of things. But when you're editing and polishing text you probably want:

• Consistent results you can rely on
• Transparency about what changed
• Speed (no waiting for API calls)
• Control over the exact transformations

That's the gap BotWash fills.

Give it a shot and see the difference between asking an AI to "make it better" and actually knowing what "better" means for your specific use case.

Try BotWash now or browse community formulas