Enhancing LLMs: 7 Context Engineering Strategies That Work in Production

Author(s): Eivind Kjosbakken

Originally published on Towards AI.

The benefits of context engineering for scaling LLMs to millions of outputs

Not a Medium member? Find this article for free below:

This article discusses the science of context engineering, detailing how to provide LLMs with the correct context to enhance their performance through techniques like retrieval-augmented generation (RAG) and few-shot prompting. It emphasizes the importance of dynamically selecting relevant examples and managing context length to avoid performance drops, shedding light on both the methodologies and implications for effectively deploying LLMs in scalable 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