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 […]

Exploring Awarity’s Elastic Context Window: A New Era in LLM Reasoning

Awarity is introducing a groundbreaking service that enhances how large language models (LLMs) reason over extensive sets of private data. We recorded this video to provide an early glimpse into the product’s capabilities. It showcases Awarity’s ability to analyze a due diligence folder containing 71 diverse documents, including Word docs, Excel files, PDFs, and PowerPoint […]

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 […]

Smarter AI for Leaner Operations: The Hybrid LLM Advantage

In today’s AI-driven business landscape, enterprises are continuously exploring ways to harness the power of LLMs to enhance operations, improve customer experiences, and make sense of vast amounts of private data. Yet, the growing complexity and computational demands of LLMs bring significant costs, prompting organizations to look for more efficient alternatives. A hybrid approach to […]

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 […]