Structured Output With Local and Cloud-Based LLMs

Author(s): Robert Martin-Short

Originally published on Towards AI.

Building robust, schema-compliant pipelines with local LLM pipelines and benchmarking against closed source models.

Fast, reliable and accurate extraction of key information from large volumes of text and image data is one of the most important use cases of LLMs in industry, as explored in this review paper.

This article focuses on structured output extraction from images, using recipes as a case study, and compares various technologies, including local and cloud-based LLM models, emphasizing the nuances of implementing these pipelines for optimal results. It discusses the challenges of ensuring consistent formatting and the rapid advancements in models that enable this type of processing. The author highlights both the capabilities and limitations of current local models versus their cloud-based counterparts, pointing to the need for iterative improvements in prompt engineering and evaluation techniques in real-world applications.

Read the full blog for free on Medium.

Join thousands of data leaders on the AI newsletter. Join over 80,000 subscribers and keep up to date with the latest developments in AI. From research to projects and ideas. If you are building an AI startup, an AI-related product, or a service, we invite you to consider becoming a sponsor.

Published via Towards AI