Why RAGStack.com?

Welcome to RAGStack.com!

The aim of RAGStack.com is to be the resource hub dedicated to Retrieval-Augmented Generation (RAG) techniques. As you might know, RAG is a key technique for building modern AI applications, chatbots and Agents. RAG combines data retrieval with language generation and allows AI applications to produce accurate responses that are context aware from your data.

RAG tools, frameworks and techniques are plentiful and it is sometimes hard to keep track of it all. Hence, the importance of staying up to date is super high as the wrong decisions can have costly consequences. Additionally, building Agents is highly dependent on high-quality, domain-specific context.

Whether you're a business leader, developer, or engineer, choosing the right approach can make a big difference. Some methods are better suited for specific use cases, while others might be more resource-intensive or harder to maintain. The goal of RAGStack is to empower you to understand the implications of using certain RAG techniques whilst incorporating your data into the AI applications.

Why RAG Matters to You?

Everyone is building Agents and everyone wants them to be useful. In order for the AI applications to be useful, they need to be personalised and the responses need to be based on your own data. In order to do that, you need to integrate your data to the LLM without giving your data to the LLM in a classic way. That's where RAG comes in.

Therefore, understanding RAG techniques is important to developers and AI engineers as it addresses some of the biggest challenges they face like improving and keeping accuracy and relevance high and keeping the responses up to date based on the most recent data.

On the other side, the opportunity in the Enterprise is huge. Business decision makers and leadership should care about RAG as it directly impacts business outcomes: it provides mode accurate, consistent and context aware responses which leads to higher customer satisfaction and acts as a competitive edge. By integrating RAG, the manual updates and needs for retraining models are reduced with increased time to market.

On the third side, even if you are building for consumer apps, you'll still want to incorporate the user's data into the mix. And you'll want it to be useful and that means that it will need to be personalised based on the user's data. So, RAG will need to come into play.

Finally, RAG solves the context problem that LLMs have where it is ill-advised and more often simply impractical for all sorts of reasons to shove all your data into the LLM context window and expect it to figure out the right answer.

How RAGStack stands out?

RAGStack is not overly technical, but is also not into oversimplification. Whether you’re new to RAG or looking to refine your approach, we’ll help you understand the landscape and impact without drowning in jargon.

RAGStack will be:

• sharing updates on trends and techniques
• diving into the thinking behind RAG and why it is the key technique for building Agents
• providing comparison of different technologies and approaches to take

Stay Up To Date

Finally, if you want to stay up to date, sign up for our newsletter. We'll keep it straightforward and relevant so you can stay again without spending too much time sifting through information.

Happy reading and welcome to RAGStack!