4 Context Engineering Techniques to Create Powerful LLM Applications

Author(s): Eivind Kjosbakken

Originally published on Towards AI.

Improve your LLM 50% by optimizing its context

Context engineering is a powerful concept you can utilize to increase the effectiveness of your LLM applications. In this article, I elaborate on context engineering techniques and how to succeed with AI applications utilizing effective context management. Thus, if you are working on AI applications utilizing LLMs, I highly recommend reading the full contents of the article.

This article covers various context engineering techniques to enhance the effectiveness of large language models (LLMs) in applications. It begins with an introduction to the importance of context management, followed by specific methods such as prompt structuring, context window management, and the use of keyword searches alongside retrieval augmented generation (RAG). The piece emphasizes the significance of evaluating LLM performance through observability and A/B testing, advocating for ongoing manual inspection to refine inputs for improved context management. In conclusion, applying these techniques can significantly boost LLM application efficiency.

Read the full blog for free on Medium.

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Published via Towards AI