We get this question a lot and so I thought I'd put together a brief definition and distinction between state, RAG, and memory.

In the rapidly evolving world of AI, understanding the core components that power advanced systems is crucial. At Backboard, we're building on a foundation of sophisticated AI capabilities, and three key concepts are central to our approach: State, RAG (Retrieval-Augmented Generation), and Memory. While these terms are often used in AI discussions, their specific application and integration within Backboard's ecosystem are what set our technology apart.

What is State?

In essence, State refers to the current condition or status of an application or system at any given moment. Think of it as the immediate context. In the realm of AI, this often pertains to the ongoing conversation, the current configuration of an AI agent, or the immediate data it's processing. Our recent launch of Alpha (Stateful API + RAG) in late 2025 underscores Backboard's commitment to effectively managing and utilizing this dynamic state, ensuring our AI can operate with real-time awareness.

What is RAG (Retrieval-Augmented Generation)?

RAG is a powerful technique that significantly enhances the knowledge base of Large Language Models (LLMs). It works by allowing an LLM to retrieve relevant information from an external data source before it generates a response. This is critical because it enables our LLMs to access and incorporate up-to-date, domain-specific, or proprietary information that they weren't originally trained on. For Backboard, integrating RAG means our AI can provide more accurate, relevant, and contextually aware outputs, drawing from the most pertinent information available.

What is Memory?

Memory is a broader and more encompassing concept than RAG. In AI, Memory refers to a system's ability to store, process, and recall past information, interactions, or experiences. This capability is fundamental for enabling:

• Conversational Continuity: Remembering previous turns in a dialogue.

• Personalized Interactions: Tailoring responses based on past user preferences or behaviors.

• Learning Over Time: Improving performance and understanding through accumulated experience.

  
  

Backboard's strategic roadmap prominently features advancements in Memory, with the planned releases of Portable Memory in October 2025 and Infinite Memory in December 2025. These initiatives highlight our dedication to developing sophisticated memory systems that allow our AI to learn, adapt, and retain context over extended periods.

The Interplay: How They Differ and Work Together

While RAG, State, and Memory are distinct, they are deeply interconnected and essential for building intelligent AI systems:

• RAG is a specific method for enriching an LLM's immediate response by accessing external data.
  
  

• Memory is a more comprehensive system for preserving and recalling past information, enabling long-term context and learning.
  
  

• State describes the current condition of the system at any given point in time, which is influenced by both RAG's retrieval and Memory's recall.

Backboard leverages the synergy between these components. RAG provides immediate, relevant data, while Memory ensures that the AI understands the ongoing context and can recall past interactions. The State of the system is continuously updated by these processes, allowing Backboard's AI to be both knowledgeable in the moment and contextually aware over time.

By mastering the interplay of State, RAG, and Memory, Backboard is building AI that is not only intelligent but also deeply understanding and continuously learning. This forms the backbone of our mission to deliver unparalleled AI solutions.