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Training, fine-tuning, and RAG (retrieval-augmented generation) all need large volumes of task-appropriate data. Web scraping is a way to collect public text, structured data, reviews, and domain information from the web — continuously, and in a form a model can use. The point isn’t to grab as many pages as possible. It’s to design which sources fit the target model, which fields to keep, how to normalize and deduplicate, and whether the collection is sound on license, terms of service, and personal data.

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Common data sources

For RAG, collect articles and documents in the target domain, then chunk the body text and store it with metadata. For a sentiment model, collect review text and scores as labeled data.

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Field and data design

Training data should keep provenance and context, not just body text. Fields to keep for training and RAG:
  • Body text (cleaned)
  • Title, headings, section structure
  • Publish and update dates
  • Category, tags, language
  • Source URL
  • Collection timestamp
Fields to keep for quality and traceability:
  • License and terms-of-service information (reuse rights)
  • Content authority signals (domain, author)
  • Hash or normalized text for deduplication
  • Collection conditions (query, scope, sampling)
Keep a source URL and collection timestamp on every row. It lets you trace the provenance of training data and later verify licenses, deduplicate, handle opt-outs, and refresh the dataset.

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Collection flow

  1. Choose data sources that fit the target model and task.
  2. Discover target pages (search, categories, sitemaps, listing pages).
  3. Extract body text and metadata (drop navigation, ads, and repeated elements).
  4. Normalize and clean the text (strip HTML, fix whitespace, unify encoding).
  5. Deduplicate (by URL, normalized text, or hash).
  6. Filter for quality (drop too-short, wrong-language, or boilerplate content).
  7. Format for training (JSONL, chunks with metadata, and so on).
Separating discovery from extraction makes it easier to process large numbers of pages in parallel.

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Quality and compliance

Data collection for AI and LLMs has to be designed for both quality and licensing.
  • Copyright and terms of service: whether body text can be reused is separate from whether it can be collected. Check licenses and each site’s terms.
  • robots.txt: check crawl permission per target URL.
  • Personal data: minimize names, contact details, and other personal data — don’t collect or store it if you don’t need it.
  • Provenance records: keep source URL and collection timestamp so you can verify licenses and handle opt-outs later.
  • Quality: exclude boilerplate, duplicates, machine-translation noise, and harmful content from training data.
Being public, and being collectable, does not mean data can be reused as training data. Handle copyright-protected body text and data containing personal information only after checking licenses and applicable law. This page is a general overview, not legal advice.

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Collecting with Octoparse and MCP

Octoparse renders pages in a headed, real browser, so text generated dynamically with JavaScript can be collected too. Build a workflow that separates discovery (listings, search) from extraction (body text, metadata), and run it on a cloud schedule to keep training data refreshed. To collect data directly from an AI agent, use Octoparse MCP Server: an MCP-capable AI client can search templates, run tasks, and retrieve data in natural language. For general content extraction, the universal content scraper template works across arbitrary web pages.