Our Knowledge Base
A curated selection of influential papers and resources that shape our approach to data and AI. We believe in standing on the shoulders of giants.
Measuring Massive Multitask Language Understanding (MMLU)
Why it matters: This paper establishes a crucial benchmark for evaluating how well AI can handle diverse, human-level tasks, guiding clients towards robust, general-purpose models.
LoRA: Low-Rank Adaptation of Large Language Models
Why it matters: LoRA provides a method for efficiently adapting large models, offering clients a faster, more affordable path to customized AI solutions without costly full retraining.
Chain-of-Thought Prompting Elicits Reasoning in Large Language Models
Why it matters: By enabling models to "think step-by-step," this technique unlocks complex reasoning capabilities, which is vital for clients tackling multi-stage analytical problems.
Sparks of Artificial General Intelligence: Early experiments with GPT-4
Why it matters: This work explores the surprisingly versatile and human-like capabilities of GPT-4, demonstrating its potential to solve novel and difficult tasks for our clients.
The Data Warehouse Toolkit by Ralph Kimball
Why it matters: A foundational guide to dimensional modeling, this book provides the essential patterns for designing data warehouses that deliver long-term business value for our clients.
Generative AI on AWS
Why it matters: This practical guide showcases how to build and deploy multimodal generative AI applications on AWS, empowering clients to leverage cutting-edge AI within a secure, scalable cloud ecosystem.
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