Grounding AI with Structure: Addressing Context Window Limitations for Reliable Outputs

Large Language Models (LLMs) have become indispensable tools for research, enabling organizations to analyze extensive datasets and generate actionable insights. However, to ensure these outputs are reliable, models must be effectively “grounded” in authoritative data—information that is accurate, trusted, and tailored to specific use cases. Grounding AI becomes particularly challenging when working with large, complex […]
Beyond Big: The End of the Hype Cycle

As AI continues to advance, the immense promise of Generative AI (GAI) has driven massive investment and excitement. However, signs are emerging that the GAI hype cycle may be nearing its peak, with economic demands beginning to take precedence over sheer technological ambition. Challenges like scaling limitations and data quality issues are fueling this […]
Overcoming RAG Limitations with Awarity’s Elastic Context Window

Introduction Retrieval-Augmented Generation (RAG) has emerged as a popular technique to enhance the capabilities of large language models (LLMs) by combining their generative power with the ability to access and retrieve information from external knowledge sources. While RAG has shown promise in various applications, scaling it to handle the massive datasets encountered in enterprise environments […]
Navigating the Challenges of Context Window Limitations

In the deployment of Large Language Models (LLMs), context windows define the amount of data a model can process at any given time. While this might sound like a minor technical detail, it directly impacts the quality of insights generated—especially when working with enterprise-level datasets that can easily reach tens of gigabytes. For many […]