Augmenting Large Language Models

Abstract

While Large Language Models demonstrate capabilities in reasoning, creativity, and task automation, they remain unable to reliably execute high-precision enterprise tasks due to inherent constraints. This article explores strategies for overcoming these constraints through tool integration, retrieval systems, and structured workflows specifically addressing issues related to static training data, computational costs, limited context windows, and hallucinations inherent to the modeling approach. Experiments show that Retrieval-Augmented Generation yields 10-percentage-point improvements in accuracy on knowledge-intensive datasets, that multi-stage prompting yields 83.5 percentage point improvements in compositional reasoning datasets, and that scaling the number of parameters from 62 billion to 540 billion yields 7.6- to 12.2 percentage point improvements on different metrics of complex reasoning. Human-AI collaboration frameworks show 20-35% productivity gains in software engineering tasks and 40-60% data efficiency gains in interactive machine learning methods. By combining retrieval methods, experimental agent architectures, fine-tuning methods, and human-in-the-loop strategies, systems have been built that use language models closely as components of a larger pipeline. This augmented intelligence model can flexibly meet enterprise-grade requirements for precision, latency, and reliability and can also accommodate continued advances across a wide variety of operational contexts.