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AI data: guides & tutorials

Curate AI training data from the web: collect, clean, dedupe, label. Apify pipelines turn crawled pages into JSON and text models can learn from.

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AI data work turns the open web into training and evaluation sets: collecting pages, cleaning and deduplicating them, then labeling at scale. These guides cover building AI-ready datasets from crawled content.

Model quality tracks data quality, so pipelines that dedupe, filter, and structure pages matter as much as the model. Apify converts crawls into JSON and text models can learn from. Below you will find tutorials on sourcing and preparing AI data.

Related topics

AI data5 min read

Mitigating Vector DB Pollution: Bypassing Consent Modals (2026)

· 5 min read
Yassine El Haddad
Software Developer & Automation Specialist

In the context of standard Data Engineering, failing to dismiss a Cookie Banner (Consent Management Platform - CMP) merely obscures the visual UI. However, in the context of Retrieval-Augmented Generation (RAG) and LLM Data Ingestion, an active CMP modal causes catastrophic architectural failure.

When an extraction script dumps raw HTML or Markdown into an embedding model before the modal is dismissed, you permanently inject ~500 tokens of strict legal boilerplate ("We value your privacy...") directly into the page_content.

If you execute this across a 10,000-page crawl, your resulting Vector Database is heavily polluted. Any semantic search querying "compliance" or "privacy" will instantly surface useless Cookie Policies instead of the actual target documents.

This guide outlines the architectural methods to programmatically bypass or eradicate CMP modals prior to executing unstructured extraction.

AI data4 min read

Apify vs Firecrawl vs Jina AI: Which Tool Fits Your Workflow (2026)

· 4 min read
Yassine El Haddad
Software Developer & Automation Specialist

Teams building RAG, agents, and data pipelines often choose between three patterns: marketplace-style scrapers for structured fields, crawl-to-Markdown APIs for LLM context, and single-URL text extraction. Apify, Firecrawl, and Jina AI Reader sit in different parts of that map. This guide compares them side by side, explains when to use which, and links to try each (including our Firecrawl affiliate link).

Quick Answer

Apify is best for scraping structured data from specific websites. Firecrawl is best for crawling websites and returning LLM-ready Markdown. Jina AI Reader is best for single-URL Markdown extraction.

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Frequently asked questions

Frequently Asked Questions

AI data is labeled or structured information used to train, fine-tune, or evaluate models - text, images, JSON records, or preference pairs scraped from sites, feeds, or internal apps. Quality beats volume: duplicates, boilerplate, and biased sources can silently hurt metrics, so provenance and cleaning pipelines matter as much as crawl breadth.

Define the schema, sample diverse URLs, then crawl with rendering where needed and normalize to UTF-8 text or JSONL. Use Apify to parallelize fetches, dedupe by content hash, and export to cloud storage; reserve 5-10% of domains for held-out evaluation so you detect overfitting to template sites.

Remove navigation chrome with readability-style extractors, strip PII you lack rights to use, language-detect and filter spam, and store raw snapshots for reproducibility. Track collection dates and robots.txt decisions; large schema-rich corpora show real prompts beat tiny synthetic sets for extraction research.

It depends on jurisdiction, site terms, copyright, and whether data is personal. Many teams restrict to opt-in feeds, licensed corpora, or public facts; consult counsel before training on scraped user-generated content. Apify helps with technical compliance like throttling and metadata, but legal review remains mandatory for commercial models.