How to build successful AI agent data?

Editor’s Note: This article shares tools and methods that can help improve AI agent performance, with a focus on data collection and cleaning. It recommends various no-code tools, such as tools that convert websites into LLM-friendly formats, as well as tools for Twitter data scraping and document summarization. Storage techniques are also introduced, emphasizing that data organization is more important than complex architectures. With these tools, users can efficiently organize data and provide high-quality input for training AI agents.

The following is the original content (for the convenience of reading comprehension, the original content has been reorganized):

Today we saw the launch of many AI agents, 99% of which will disappear.

What makes a successful project stand out? Data.

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

Good data=good AI.

Think of it as a data scientist building a pipeline:

Collect → Clean → Verify → Store.

Before optimizing the vector database, adjust your few sample instances and prompts.

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I see most of today’s AI problems as Steven Bartlett’s “bucket theory” - solve them step by step.

First, lay a solid data foundation, which is the cornerstone of building an excellent AI agent pipeline.

Here are some excellent tools for data collection and cleaning:

No-code llms.txt generator: Convert any website into text suitable for LLM.

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Need to generate LLM-friendly Markdown? Try JinaAI’s tools:

Use JinaAI to crawl any website and convert it into Markdown format suitable for LLM.

Just add the following prefix to the URL, and you can get an LLM-friendly version:

Want to access Twitter data?

Try the twitter-scraper-finetune tool of ai16zdao:

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

(Check my previous tweets for specific instructions)

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Data source recommendation: elfa ai (currently in closed testing phase, please DM tethrees to get access permission)

Their API provides:

Most Popular Tweets

Intelligent fan selection

The latest $ mentioned content

Account credibility check (used to filter spam content)

Perfect for high-quality AI training data!

For document summary: Try Google’s NotebookLM.

Upload any PDF/TXT file → Let it generate few sample examples for your training data.

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

Storage Tips:

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

If you run Eliza of ai16zdao, you can directly store the data into vector storage.

Professional advice: Well-organized data is more important than fancy architecture!

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