Llama passive voice fixer

Text drafted with a Llama-based model carries a risk more likely here than with a single closed model: inconsistent passive voice usage across one document, where one section reads heavily passive and formal and another reads blunt and active, because different portions may trace to different fine-tunes or sampling settings. This tool corrects the inconsistency and leaves correctly used passive voice untouched.

The analysis happens server-side, since judging which passive constructions need correction requires genuine reading rather than a mechanical substitution. The allowance is 5 free runs per rolling 24-hour window, with no account required.

Because Llama is open-weight, a document assembled from more than one session, or produced by a deployment that shifted behaviour across a long generation, can carry two distinct defaults: one section heavily hedged and passive, in the style some instruction-tuned fine-tunes favour, and another blunt and active, produced by a differently tuned or differently sampled pass.

Neither default is incorrect in isolation. The problem is carrying both within the same document without editorial reason, which reads as inconsistent rather than deliberate. The actual correction is narrower than passive voice itself: agentless passive that drops responsibility, and passive applied inconsistently rather than by genuine editorial choice.

The process is intentionally short: three steps, no configuration required.

Step 1

Paste the Llama-drafted text, in full or by section.

Step 2

Run it. The text is sent to our AI server, which locates passive constructions and evens out inconsistent usage across the passage.

Step 3

Review the result against the source. Sentences where passive voice is contextually correct are left unchanged.

A document that shifts between heavily hedged, passive prose and blunt, active statements reads as though two different writers produced it, undermining the impression of one coherent voice even when the underlying facts are all correct.

The agentless variant costs more wherever it appears: a sentence that quietly omits who acted reads as evasive regardless of intent, particularly against surrounding paragraphs that were confidently active.

Passive voice is the right construction in a defined set of circumstances, and the fixer is built to recognise them rather than flatten every passive sentence into active voice or force every active sentence into passive.

The test: is the agent unknown, irrelevant to the point, or deliberately de-emphasised because the result matters more than who performed it? Scientific writing relies on passive constructions for precisely this reason, and that convention holds regardless of which Llama deployment or fine-tune produced the sentence.

The failure mode is not passive voice itself. It is agentless passive avoiding a responsible party, and inconsistent usage tracking which part of the pipeline produced which paragraph rather than genuine editorial reasoning.

No, and correcting passive voice consistency was never going to accomplish that alone. We do not test this tool against AI-detection services or tune it against any detector's method, so we have no basis for a claim about what a detector would conclude.

The verifiable effect is that the document is brought to one consistent standard for passive voice, with agentless passive that dodges attribution corrected along the way. That improvement stands independently of any claim about detection.

Developers and hobbyists running local Llama-based models who noticed a document reads inconsistently, particularly after combining output from more than one session or fine-tune.

Writers and editors revising a Llama-assisted draft who want one consistent standard applied across the whole document rather than whatever each section happened to inherit.