Retrieval-Augmented Generation (RAG) pipelines live and die by their embeddings. If you feed raw, unoptimized web data into a text chunker, your vector database will be poisoned by navigation menus, footer links, cookie banners, and inline CSS.

Naive implementations often request an HTML page, run a regex to strip tags, and pass the resulting text wall into a character splitter. This destroys structural context. A chunk might end mid-sentence, or worse, blend a critical paragraph with a site's privacy policy. When the LLM retrieves this context, the output hallucinates or misses the point entirely.

To build accurate RAG pipelines, data optimization must happen before chunking. You need a systematic approach to extract clean, semantically intact content from public web sources.

Phase 1: Reliable Data Ingestion

Modern web applications are client-side rendered. A simple HTTP GET request often returns an empty root div and a bundle of JavaScript.…