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See how Firecrawl turns messy websites into clean, structured data that LLMs can actually reason with. No Puppeteer. No CSS selectors. One API call.
From someone who built scrapers for years: I built and maintained Pageripper, a commercial web scraping API that used Puppeteer to handle JavaScript-heavy sites. The infrastructure headaches were real — browser memory leaks, selector rot, proxy management. Firecrawl solves the exact same problems I spent years wrestling with, but as a managed API that delivers clean markdown and structured data ready for AI pipelines.
Watch Firecrawl spider through a site, following links and discovering pages
Firecrawl strips ads, navigation, scripts, and boilerplate — leaving only content
<!DOCTYPE html>
<html>
<head>
<title>Acme AI Blog</title>
<script src="bundle.js"></script>
<link rel="stylesheet" href="styles.css">
<meta name="viewport" content="...">
</head>
<body>
<nav class="header">...</nav>
<div id="app">
<article class="post">
<h1>Building RAG Pipelines</h1>
<div class="meta">
<span>Jan 15, 2026</span>
<span>12 min read</span>
</div>
<p>Retrieval-Augmented Generation
combines the power of LLMs with
external knowledge bases...</p>
<div class="sidebar">
<div class="ad">Buy now!</div>
<div class="related">...</div>
</div>
</article>
</div>
<footer>...</footer>
<script>analytics.track()</script>
</body>
</html># Building RAG Pipelines
**Published:** Jan 15, 2026 | **Read time:** 12 min
Retrieval-Augmented Generation combines the
power of LLMs with external knowledge bases
to produce grounded, factual responses.
## Why RAG Matters
Traditional LLMs are limited to their training
data. RAG lets you connect them to live,
up-to-date sources...
## Key Components
1. **Document ingestion** - crawl and clean
2. **Chunking** - split into passages
3. **Embedding** - vectorize for retrieval
4. **Generation** - LLM + retrieved context
[Read more](/blog/rag-pipeline)
{
"title": "Building RAG Pipelines",
"author": "Acme AI Team",
"date": "2026-01-15",
"readTime": "12 min",
"content": "Retrieval-Augmented Generation...",
"links": ["/blog/rag-pipeline"],
"metadata": {
"language": "en",
"wordCount": 2847,
"topics": ["RAG", "LLM", "AI"]
}
}I built scrapers for years. Here's what you're signing up for with DIY
import Firecrawl from '@mendable/firecrawl-js'
const app = new Firecrawl({ apiKey: 'fc-...' })
// Crawl an entire site
const result = await app.crawlUrl('https://acme.com', {
limit: 100,
scrapeOptions: {
formats: ['markdown', 'html']
}
})
// Each page: clean markdown + metadata
result.data.forEach(page => {
console.log(page.markdown) // Clean content
console.log(page.metadata) // Title, desc, links
})Clean web data unlocks powerful AI applications
Feed clean, structured web content into your vector database. Firecrawl strips nav, ads, and boilerplate so your embeddings contain only signal.
Crawl docs.example.com → 847 pages → Clean markdown → Chunk & embed → Vector DB → Your AI answers questions about the docs
Firecrawl handles the crawling, rendering, and extraction so you can focus on what matters — building intelligent applications with clean data.