Ai Blueprint

AI blueprint

A blueprint for AI development, focusing on applied examples of RAG, information extraction, and more in the age of LLMs and agents. It is a practical approach that strives to show the application of some of the more theoretical learnings from [the smol-course](https://github.com/huggingface/smol-course) and apply them to an end2end real-world example.

🚀 Web apps and microservices included!

Each notebook will show how to deploy your AI as a [webapp on Hugging Face Spaces with Gradio](https://huggingface.co/docs/hub/en/spaces-sdks-gradio), which you can directly use as microservices through [the Gradio Python Client](https://www.gradio.app/guides/getting-started-with-the-python-client). All the code and demos can be used in a private or public setting. [Deployed on the Hub!](https://huggingface.co/ai-blueprint)

The problem statement

We are a company want to build AI tools but we are **not sure where to start**, let alone how to get things done. We only know that **we have a lot of valuable data** and that AI could help us get more value out of it. We have uploaded them to [ai-blueprint/fineweb-bbc-news](https://huggingface.co/datasets/ai-blueprint/fineweb-bbc-news) on the Hugging Face Hub and want to use it to start building our AI stack.

Manager: "Establish a simple baseline and iterate from there!"

Retrieval Augmented Generation (RAG)

RAG (Retrieval Augmented Generation) is a technique that helps AI give better answers by first finding and using relevant information from your documents. Think of it like giving an AI assistant access to your company's knowledge base before asking it questions - this helps it provide more accurate and factual responses based on your actual data.


Common use cases

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