How to create successful AI agent data?

Original author: jlwhoo7, Crypto Kol
Original translation: zhouzhou, BlockBeats

Editor's note:This article shares tools and methods that help improve the performance of AI agents, with a focus on data collection and cleaning. A variety of no-code tools are recommended, such as tools for converting websites to LLM-friendly formats, and tools for Twitter data crawling and document summarization. Storage tips are also introduced, emphasizing that the organization of data is more important than complex architecture. With these tools, users can efficiently organize data and provide high-quality input for the training of AI agents.

The following is the original content (the original content has been reorganized for easier reading and understanding):

We see many AI agents launched today, 99% of which will disappear.

What makes successful projects stand out? Data.

Here are some tools that can make your AI agent stand out.

Good data = good AI.

Think of it like a data scientist building a pipeline:

Collect → Clean → Validate → Store.

Before optimizing your vector database, tune your few-shot examples and prompt words.

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I view most of today’s AI problems as Steven Bartlett’s “bucket theory” — solving them piece by piece.

First, lay a good data foundation, which is the foundation for building a good AI agent pipeline.

Here are some great tools for data collection and cleaning:

Code-free llms.txt generator: convert any website to LLM-friendly text.

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Need to generate LLM-friendly Markdown? Try JinaAI's tool:

Crawl any website with JinaAI and convert it to LLM-friendly Markdown.

Just prefix the URL with the following to get an LLM-friendly version:
http://r.jina.ai<URL>

Want to get Twitter data?

Try ai16zdao's twitter-scraper-finetune tool:

With just one command, you can scrape data from any public Twitter account.

(See my previous tweet for specific operations)

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Data source recommendation: elfa ai (currently in closed beta, you can PM tethrees to get access)

Their API provides:

Most popular tweets

Smart follower filtering

Latest $ mentions

Account reputation check (for filtering spam)

Great for high-quality AI training data!

For document summarization: Try Google's NotebookLM.

Upload any PDF/TXT file → let it generate few-shot examples for your training data.

Great for creating high-quality few-shot hints from documents!

Storage Tips:

If you use virtuals io's CognitiveCore, you can upload the generated file directly.

If you run ai16zdao's Eliza, you can store data directly into vector storage.

Pro Tip: Well-organized data is more important than fancy schemas!

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