AIRAGTutorial

How to Build a RAG Dataset in 2 Minutes (No Code)

Scrape, clean, and chunk websites into AI-ready datasets without writing a single line of code. Perfect for LLM fine-tuning and RAG pipelines.

June 10, 20264 min read

Building a RAG (Retrieval-Augmented Generation) dataset used to mean writing scrapers, cleaning HTML, chunking text, and formatting JSONL — a full weekend of engineering work.

Not anymore.

With BotraAI's free Dataset Builder, you can go from a list of URLs to a production-ready dataset in two minutes. Here's how.

Step 1: Gather Your URLs

Start with 5–50 URLs that contain the knowledge you want your AI to learn:

Documentation pages — Stripe docs, Python docs
Product pages — Competitor features, pricing
Knowledge base articles — FAQs, how-to guides
Blog posts — Technical tutorials, case studies

Pro tip: Mix sources. A diverse dataset produces better RAG results than a single-domain one.

Step 2: Paste URLs into the Dataset Builder

Head over to the AI Dataset Builder tool. Paste your URLs — one per line. Choose your settings:

Chunk size:** 512 tokens (good default for most RAG systems) —
Export format:** JSONL (standard for OpenAI, Anthropic, and most vector databases) —

Click Build Dataset.

Step 3: Review and Export

In under 30 seconds, you'll have scraped content from every URL, cleaned text with no HTML or ads, chunked data at semantic boundaries, deduplicated content, and JSONL formatted output ready for ingestion.

Download the file and feed it directly into your vector database or fine-tuning pipeline.

What You Can Build With This

RAG chatbot	JSONL	Pinecone, Weaviate, Chroma
LLM fine-tuning	JSONL	OpenAI, Anthropic
Knowledge base	Markdown	Notion, Obsidian
Data labeling	CSV	Label Studio, Argilla

Why BotraAI vs Manual Scraping

Write scraper scripts	Paste URLs
Handle rate limiting	Done automatically
Clean HTML by hand	AI-powered extraction
Chunk manually	Smart semantic chunking
Debug for hours	Done in 2 minutes

Ready to build your first dataset?

Try it yourself — free, no signup required

Try the Dataset Builder

Back to Blog

AIRAGTutorial

How to Build a RAG Dataset in 2 Minutes (No Code)

Scrape, clean, and chunk websites into AI-ready datasets without writing a single line of code. Perfect for LLM fine-tuning and RAG pipelines.

June 10, 20264 min read

Building a RAG (Retrieval-Augmented Generation) dataset used to mean writing scrapers, cleaning HTML, chunking text, and formatting JSONL — a full weekend of engineering work.

Not anymore.

With BotraAI's free Dataset Builder, you can go from a list of URLs to a production-ready dataset in two minutes. Here's how.

Step 1: Gather Your URLs

Start with 5–50 URLs that contain the knowledge you want your AI to learn:

Documentation pages — Stripe docs, Python docs
Product pages — Competitor features, pricing
Knowledge base articles — FAQs, how-to guides
Blog posts — Technical tutorials, case studies

Pro tip: Mix sources. A diverse dataset produces better RAG results than a single-domain one.

Step 2: Paste URLs into the Dataset Builder

Head over to the AI Dataset Builder tool. Paste your URLs — one per line. Choose your settings:

Chunk size:** 512 tokens (good default for most RAG systems) —
Export format:** JSONL (standard for OpenAI, Anthropic, and most vector databases) —

Click Build Dataset.

Step 3: Review and Export

Download the file and feed it directly into your vector database or fine-tuning pipeline.

What You Can Build With This

RAG chatbot	JSONL	Pinecone, Weaviate, Chroma
LLM fine-tuning	JSONL	OpenAI, Anthropic
Knowledge base	Markdown	Notion, Obsidian
Data labeling	CSV	Label Studio, Argilla

Why BotraAI vs Manual Scraping

Write scraper scripts	Paste URLs
Handle rate limiting	Done automatically
Clean HTML by hand	AI-powered extraction
Chunk manually	Smart semantic chunking
Debug for hours	Done in 2 minutes

Ready to build your first dataset?

Try it yourself — free, no signup required

Try the Dataset Builder

Back to Blog