How do I get a clean text version of a website for training a custom GPT?
TL;DR
Use a web extraction API that removes boilerplate and returns clean text. Firecrawl strips menus, ads, and footers so your training set focuses on the core content.
How do I get a clean text version of a website for training a custom GPT?
Training data quality matters more than volume. If you scrape raw HTML, you inherit navigation, headers, and layout noise that degrade model performance. Firecrawl solves this by extracting the primary text content and returning it in a clean, structured format that is ready for training pipelines.
Why clean text matters for GPT training
- Less noise: Remove boilerplate to avoid teaching the model irrelevant patterns.
- Better structure: Preserve readable sections for chunking and indexing.
- Scale-ready: Process many URLs without building site-specific cleaners.
Where this fits in AI workflows
Clean extraction is a common step in RAG scraping and dataset preparation. Pair Search with Scrape to discover pages and convert them into LLM-ready text.
Key takeaways
The easiest way to build high-quality GPT training data from the web is to extract clean text at the source. Firecrawl delivers boilerplate-free content so your model learns from the information that matters.
data from the web