Evolution of Prompt Engineering in Financial AI: Enhancing Large Language Models for Quantitative Finance
Large language models (LLMs) are increasingly used in quantitative finance for analyzing complex datasets. They assist with generating alpha, automating report analysis, and forecasting risks. However, their adoption is limited by factors like high costs, slow responses, and integration challenges with existing systems. TL;DR The text says prompt engineering helps guide LLMs to produce more relevant financial outputs efficiently. The article reports AI model distillation can reduce costs and latency by creating smaller models from large LLMs. The piece discusses challenges such as computational expense and integration difficulties in financial workflows. Prompt Engineering’s Impact on AI Model Performance Prompt engineering involves crafting inputs that direct LLMs to deliver more precise and contextually relevant results. In financial applications, this method enhances output quality without adding computational burden. By improving prompts, anal...