we talk a lot about what paraai does. let's talk about how.

the base model

we start with state-of-the-art large language models. the same caliber of models that power chatgpt and claude. these are strong general-purpose models with broad language understanding.

but a base model out of the box produces text with ai-typical patterns. that's the whole problem. so the base model is just the starting point.

the fine-tuning

this is where the magic happens. we fine-tune these models on curated datasets of human-written text.

not random internet text. curated corpora of verified human writing across different styles, contexts, and skill levels. student essays. blog posts. journalism. casual writing. formal reports. the full spectrum of how real people actually write.

the fine-tuning process adjusts the model's weights so its generation patterns shift toward human norms. the perplexity distribution changes. the burstiness increases. the vocabulary choices become more varied and less predictable.

after fine-tuning, the model generates text that statistically resembles human writing — not because of rules we coded, but because it learned from the data what human writing looks like.

what happens when you paste text

when you paste text into paraphrase and hit the button, here's the sequence:

1. comprehension. the model reads your input text and builds an internal representation of the meaning. not word by word — semantically. it understands what you're saying, not just the words you used.

2. regeneration. using the meaning representation, the model generates new text that expresses the same ideas. this is full generation, not modification. the model is writing new sentences from scratch based on what the input means.

3. pattern matching. because the model was fine-tuned on human text, the generated output naturally has human writing patterns. varied sentence lengths. unpredictable word choices. natural rhythm. the model doesn't add these as a post-processing step — they emerge from the fine-tuned weights.

the result: text that says the same thing as your input but reads completely differently at the surface level.

why this beats alternatives

synonym swappers work at the word level. they don't understand meaning so they can't restructure sentences or change patterns. the document-level statistics barely change.

rule-based humanizers add variation through coded rules — "randomly vary sentence length," "insert informal phrases." the variation is artificial and detectors learn to spot it.

prompt-based approaches ask a base model to "write like a human." the model tries but its underlying patterns are too strong. you get slightly less formal ai text. still detectable.

fine-tuned models have genuinely different generation patterns. the variation is natural because it came from natural data. detectors see human patterns because the model produces human patterns. it's not a trick — it's how the model was trained.

the detection results

raw chatgpt output: 90-99% ai on all major detectors. after paraai paraphrase: 2-10% ai on all major detectors.

the gap isn't because we found a clever hack. it's because the output has fundamentally different statistical properties. the text is genuinely closer to human writing at a measurable level.

what we're still improving

we're always refining the training data, experimenting with fine-tuning approaches, and testing against new detectors. the models get better with each iteration.

untraceable ai writing is our core mission. every engineering decision we make is in service of that one goal — producing text that reads like a human wrote it, because the model that generated it learned from humans how to write.