Long-Context LLMs, Data Push and LLM Single-Tenancy
Will the shift to LCLMs and data pull architectures make data push techniques like RAG obsolete?
The Expansion of LLM Context Windows
One of the most notable LLM trends is the steady increase in context window size. LLMs are becoming capable of handling more documents and context within a single request.
The implications of this trend are significant. As LLMs become more efficient in processing long-form context, the relevance of retrieval-augmented generation (RAG) techniques may diminish. The ability to push more data directly to the LLM, combined with the model’s inherent capabilities to pull relevant information, can lead to the emergence of powerful new pipelines and enhanced performance in chat-focused use cases.
The Shift Towards Data Pull Architectures
Traditionally, the predominant approach to feeding data to language models has been through “data push” methods, such as retrieve and build context first, then feed it to the LLM.
However, as LLMs continue to evolve, incorporating larger context windows and native agent-like capabilities, a shift towards “data pull” may occur. LLMs will actively seek and retrieve relevant information from various data sources, rather than relying solely on data being pushed to them.
The Demand for LLM Single-Tenancy
As enterprises increasingly recognize the value of their proprietary data, concerns regarding data privacy and sharing mount. Many organizations are hesitant to share their sensitive information with multi-tenant LLMs, fearing potential breaches or unintended exposure.
What to expect from a growing demand for LLM single-tenancy solutions? Could it be that the “Big 3” LLM providers will start offering dedicated, isolated instances of their models?
If they will, the combination of long context and single-tenancy deployments has the potential to accelerate the adoption of data pull architectures. LLMs will be able to directly access and “see” the data where it resides, reducing the need for complex data push pipelines. This shift may also drive the development of native data connectors within LLMs, further simplifying the integration process.
What’s next
As LLMs continue to develop, we can expect to see significant changes in the way data is managed, processed, and fed into these models.
The growing demand for LLM single-tenancy in enterprise settings will further shape the evolution of these technologies. Being able to satisfy the need for managed single-tenancy solutions, encompassing data and LLMs, will become a key differentiator.