Author(s): Eivind Kjosbakken

Originally published on Towards AI.

Learn how to apply context engineering to enhance your question answering systems.

I believe context engineering is one of the most relevant topics in machine learning today, which is why I’m writing my third article on the topic. My goal is to both broaden my understanding of engineering contexts for LLMs and share that knowledge through my articles.

This article discusses the importance of context engineering in optimizing question answering systems by enhancing the context input to large language models (LLMs). It critiques traditional retrieval augmented generation (RAG) methods and explores new techniques to improve context handling, including reducing irrelevant tokens and leveraging better embedding models for improved output quality and efficiency. The author provides actionable insights for optimizing LLMs, emphasizing the balance between precision and recall in context fetching for question answering 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