In recent discussions surrounding advancements in AI technology, particularly in the realm of Large Language Models (LLMs), I’d like to expand upon some key terminologies employed in these conversations. This is especially relevant for those who may not be well-versed in the industry.
There are a couple of critical terms to clarify:
- Persistence: This refers to durable storage of application state.
- Planning: This is a type of agent workflow that utilizes Retrieval-Augmented Generation (RAG) context fed to the LLM or frontier models. It involves checkpoints that can save (“persist”) the state of the response, allowing users to resume, fork, or modify from that checkpoint with the state data available at that time.
The concept being discussed here resembles a variant of the external RAG connector paradigm mentioned in my previous comment. Essentially, the model ingests information stored within the user’s application space—such as a project plan detailing goals, outcomes, and examples of expected results—and processes this using a planning or orchestration agent. Consequently, the model can take action based on the project plan’s contents, alongside any other external tools, including web searches or scheduling applications. This marks a significant transition in the AI landscape, evolving from experimental stages into a mainstream component of application layers.
Addressing the issue of hallucinations, these typically emerge when the model lacks sufficient context for its queries, prompting it to generate responses to avoid errors. Enhancing context through RAG—while carefully balancing the amount to fit within the model’s context window—constitutes the majority of effective AI utilization.
It’s crucial to note that no groundbreaking breakthroughs are being described here. Most serious developers in the AI sector are integrating more advanced RAG-based tools within their applications, utilizing cutting-edge frontier models. This adoption creates a bullwhip effect as users learn to navigate new tiers of tools; paradoxically, as more tools are incorporated, the reliance on deep training sets by the traditional LLMs diminishes. Over time, we may witness the decline of large frontier models, supplanted by highly specialized models tailored for specific applications, such as coding. We’ve already started to see this in domains where coding types lack the wealth of public data used for training frontier models, particularly in areas like FPGA/ASIC development.
To clarify an important point: any reputable company deploying this technology will establish a user-associated persistence layer independent of the model, ensuring that each user’s context files remain separate (a practice known as “multitenancy”). Enterprise users typically enter legal agreements to ensure their data remains non-reusable for retraining frontier models, especially when the service provider is also a frontier model vendor. User context is usually encrypted and formatted for machine readability, thereby safeguarding against unauthorized data usage. However, not all paid services offer this level of protection, emphasizing the importance of scrutinizing agreements carefully. Providers that leverage Microsoft services for building their applications will need to channel private, secure endpoints, ensuring a chain of legal accountability that allows for thorough oversight. Unfortunately, this security is generally absent in free and public chatbot implementations, where many negative experiences originate.
In summary, as AI technology progresses, the integration of advanced tools continues to reshape how we interact with these systems. Understanding terminology, data security, and the evolving landscape is essential for leveraging AI effectively. As we move forward, it’s crucial to remain aware of the implications of these advancements in the broader context of digital applications.
