Today, we are very excited to announce the release of our new book “A Practical Approach to Retrieval Augmented Generation Systems”!
Why did we do this?
Short answer: Thriving community and passion from the makers!
Longer answer: Do you know that bootstrapping Indie Hackers like PDF.AI is generating $30k MRR? We noticed that there are a lot of makers who are rolling up their sleeves trying out LLM APIs and building RAG systems.
We believe this is the future.
We believe the future companies will be small and nimble teams + passion + AI.
We are one right here!
Most importantly, you could do the same!
All you need is just burning passion of building things for the greater good, and taking control of your own career!
What do we talk about in this book?
We show you how RAG works in the real world, using the popular use case of “chatting with your PDF documents”, building from data ingestion, extracting information from PDF documents, all the way to retrieval and generation of your responses. We talk about challenges along the way and potential solutions for building production-ready applications. So a more practical approach.
We have tried different frameworks including LLamaIndex, LangChain, and Haystack, among the most popular ones. We would like to share our usage with you, so you can make the right choice for your specific needs.
🚀 Launch Sale
If you’re ready to join us in this exciting journey to master the art of Retrieval-Augmented Generation, we invite you to take advantage of our limited-time launch sale, at $15 (51% off the regular price of $29)!
Join the community of RAG enthusiasts and practitioners who are already enhancing their AI systems and applications. Whether you’re a developer, a data scientist, an AI researcher, or just curious about the future of AI, this book has something to offer you.
A Shoutout to the Community
In our exploration of RAG, we couldn’t have done it without the support of the incredible open-source community. We would like to express our gratitude to the teams behind LLamaIndex, LangChain, and Haystack for their invaluable contributions to the field of RAG. Their comprehensive documentations and tutorials have been instrumental in our journey, allowing us to learn from their expertise and leverage the fascinating tools they have built.
Are you ready?
Happy practicing and happy building!