How does it detect redundancy without an AI model?

It uses TF-IDF cosine similarity. Each sentence becomes a weighted vector of its words; pairs whose vectors point in nearly the same direction are flagged as overlapping. It is fully deterministic and runs in your browser with no network calls.

Why not use embeddings for true semantic similarity?

Embeddings need a model and an API call. TF-IDF catches the most common kind of prompt bloat — the same instruction restated with overlapping words — without sending your prompt anywhere. It will miss pure paraphrases that share no vocabulary, which is the documented trade-off.

Will trimming redundancy hurt the prompt?

Sometimes intentional repetition reinforces a critical rule. The tool only flags candidates; you decide what to cut. Keep deliberate emphasis and remove accidental restatement.

Is my prompt sent anywhere?

No. All tokenization and similarity scoring happens locally in your browser. Nothing is uploaded or stored.

Prompt Redundancy Remover

Prompt redundancy remover

Long system prompts accumulate cruft. You add a rule, then three edits later you add nearly the same rule again in different words. Every duplicate sentence costs tokens on every single call and can even confuse the model with conflicting phrasings. This tool scans your prompt for sentence pairs that say the same thing and flags them so you can cut the bloat.

How it works

The tool splits your prompt into sentences and converts each one into a TF-IDF vector — a weighted bag of words where common, uninformative words count for little and distinctive words count for more. It then computes the cosine similarity between every pair of sentences. Pairs above your threshold point in nearly the same direction in word space, which means they share most of their meaningful vocabulary — the signature of a restated instruction. Everything runs locally; your prompt never leaves the page.

Tips and notes

Tune the threshold. 0.6 is a good start. Raise it toward 0.8 if you get too many loose matches; lower it to surface softer paraphrases.
TF-IDF is lexical, not semantic. It catches “always respond in JSON” vs “your responses must always be JSON” but will miss two sentences that mean the same thing with zero shared words. That is the documented limitation.
Keep intentional repetition. Repeating a hard safety rule on purpose is fine — only remove accidental duplication.
Re-run after editing. Trim a pair, paste the result back, and scan again to catch second-order overlaps the first pass hid.