When dealing with real-world data from the web, especially through browsers, heuristics are still essential.
The modern web is messy. Pages are bloated with structural noise — markup, layout fragments, irrelevant text. Sending raw page data to a model wastes tokens and processing time. At scale, this becomes expensive and inefficient.
At Warp, we aggressively compress and reduce content before inference. We shrink a raw webpage to under 3% of its original size before passing anything to a model. Under 1% if its a modern post with not much textual substance.
This is not just optimization — it’s necessity.
We apply domain-specific heuristics to identify high-signal areas of text, discard layout-driven elements, and collapse repetitive structures.
After removing non-content tags like <script>, <style>, <img>, <svg>, and other structural or decorative elements (meta, iframe, noscript, etc.), and stripping out HTML entities, we’re typically left with about 20% of the original page size — most of which is whitespace interspersed with sparse clusters of actual text. From there, we apply heuristics to remove small, low-value clusters and discard empty gaps, isolating a set of meaningful text blocks. Each block generally contains several sentences or full paragraphs. This process alone achieves a 97%+ reduction in content size. Since our architecture targets edge execution and can’t always rely on a local LLM, we use sentence embeddings to compare the semantic similarity of these blocks. We retain only the relevant ones and collapse the array into a final, dense output — highly compressed, highly relevant, and model-ready.
Benefits are tenfold :
- this kind of preprocessing is lightweight
- its deterministic
- makes downstream inference faster ( less tokens )
- …cheaper ( less tokens )
- …more reliable ( its deterministic )
- keeps models focused
In an age dominated by large language models, it’s tempting to rely on AI to handle everything – from parsing content to understanding structure, but the Production is a cold shower to an average AI researcher.
Keep your pipelines efficient… that is the only way your AI enabled service can earn money.