The Evolution of AI Memory: RAG is dead. Long live RAG.

As we continue to push the boundaries of artificial intelligence, the importance of effective memory and storage state management has become increasingly evident. I recently wrote an article evaluating this as it applies to the dramatic leap in context window size from Gemini 1.5.?

?Traditional RAG (Retrieval Augmented Generation) approaches have served us well, but their limitations have become apparent. In this article, I'll dive into the technical aspects of RAG's demise and the rise of contextual language models (CLMs) like RAG2, exploring how these innovations are making dramatic advances in AI memory and accuracy. Additionally, we'll evaluate how CLMs will interact with fit-for-purpose models and discuss the impact on data centers and storage technology.

?The RAG Conundrum: A House of Cards

Imagine building a house of cards. Each card represents a piece of information, and the structure symbolizes the relationships between them. Traditional RAG approaches are like building a house of cards with a limited number of cards and a rigid structure. As the amount of information grows, the house becomes unstable, and the relationships between cards become increasingly difficult to manage.

?RAG's limitations can be attributed to its reliance on:

1. Fixed context windows: RAG can only process a limited amount of context, making it challenging to understand nuanced and complex relationships between pieces of information.
2. Frozen knowledge: RAG's knowledge base is static, making it difficult to adapt to new information and evolving contexts.
3. Lack of common sense: RAG lacks real-world experience and common sense, leading to responses that may be technically correct but contextually inappropriate.
4. RAG failure: RAG can fail to find relevant information because of fatigue of resources or completely abandon the search because it found a relevant vector based on the prompt that said "give up" or something like that, while the real data is deeper into the corpus.

The Rise of Contextual Language Models: A Dynamic Library

CLMs like RAG2 from Contexual.ai are revolutionizing AI memory again by introducing a dynamic library approach. Imagine a library where books (pieces of information) can be added, removed, and rearranged as needed. This library is equipped with an intelligent librarian (the model) who can connect books in innovative ways, understand context, and provide accurate recommendations.

?CLMs address the limitations of traditional RAG approaches by:

1. Expanding context windows: CLMs can process longer context windows, enabling a deeper understanding of complex relationships between pieces of information.
2. Adapting to new knowledge: CLMs can incorporate new information and adapt to evolving contexts, ensuring their knowledge base remains relevant and up-to-date.
3. Developing common sense: CLMs are designed to learn from real-world experiences and develop common sense, enabling them to provide more contextually appropriate responses.

Technical Advantages of CLMs

CLMs like RAG2 boast several technical advantages over traditional RAG approaches:

1. Transformer-based architecture: CLMs employ transformer-based architectures, which enable parallel processing and efficient handling of long-range dependencies.
2. Attention mechanisms: CLMs use attention mechanisms to focus on relevant pieces of information, ensuring accurate context understanding and response generation.
3. Generative capabilities: CLMs can generate text, enabling them to provide more comprehensive and accurate responses.

Impact on Data Centers and Storage Technology

The rise of CLMs will significantly impact data centers and technology, driving the need for:

1. Scalable storage solutions: CLMs require vast amounts of storage to accommodate their expanding knowledge bases and context windows.? Most consumers of storage today purchase their storage in "chunks" at a time based on a look back growth analysis and some fuzzy input from project management and business leadership.? ?This frankly does not make sense and hasn't for quite a while now for shareholder value.? ?We should be subscribing to storage in a cloud-like model.? Even companies who are not OpEx-friendly in their budget process have options to make this look like a CapEx purchase on the books.? Pay for what you need, when you need it, and pick a solution that can meet your scale.
2. High-performance computing: CLMs demand powerful processing capabilities to handle complex relationships and generate responses, but you must stay abstracted from the hardware.? ?Compute should be treated like a commodity.? VMware helped accomplish this over 20 years ago when paired with Boot-from-SAN infrastructure architecture designs.? ?With that abstraction, I can now refresh compute and take advantage of high clock speeds, improved memory density, and new computing technologies like additional L1-3 cache techniques to stay on the curve and avoid a lengthy, customer-impacting refresh cycle.
3. Advanced data management: CLMs necessitate sophisticated data management systems to ensure efficient knowledge retrieval and updating.? This is where Kubernetes comes into play.? By staying abstracted as I said above and using enterprise-grade storage subsystems like Portworx by Pure Storage to manage storage state and provide resiliency and availability, regardless of orchestrator or locality, you will have a modern architecture built to support not only your legacy systems and architecture designs but also your future ones as well that will most certainly leverage RAG1.0/2.0 techniques.
4. Energy efficiency: Data centers must prioritize energy efficiency to mitigate the environmental impact of CLMs' increased computational requirements. ?With a typical power consumption of 1,400 watts compared to the 9,100 watts of competitive products, Pure Storage demonstrates a watt per TB (effective) of less than 1, which is significantly lower than the 4 watts per TB of its competitors. ?NVIDIA's introduction of energy-efficient AI chips like the Blackwell platform marks a significant step forward, enabling organizations to build and run real-time generative AI on large language models at a fraction of the cost and energy consumption.? I cover this topic sufficiently in my article The Evolution of AI Efficiency: From Functionality to Sustainable Power Use.

Conclusion

The evolution of AI memory is a critical aspect of advancing artificial intelligence. Contextual language models like RAG2 are revolutionizing the way we approach AI memory, enabling more accurate, efficient, and contextually appropriate responses. As CLMs continue to advance, data centers and architecture designs must adapt to support their growing demands, ensuring a harmonious and efficient relationship between AI innovation and infrastructure. Join the conversation and let's shape the future of AI together!