Prompt Engineering Guide for Open LLM: Take Your Open LLM Application to the Next Level

Author(s): Timothy Lim

Originally published on Towards AI.

Introduction: Why do we need another guide?

Numerous prompt engineering guides have already been written. However, the majority of them focus on closed-source models characterized by their immense capacity, robust reasoning capabilities, and comprehensive language understanding.

The purpose of this blog is to address Prompt Engineering for open-source Language Model Models (Open LLMs), specifically within the parameter range of 3 to 70 billion. Despite prevailing notions in various posts, these Open LLMs are incomparable to their closed-source counterparts.

You may have read misleading articles such as “ChatGPT Clone for Just $300”, “An Open-Source Chatbot Impressing GPT-4 with 90%*ChatGPT Quality”, but the truth is, when you are building an application, the differences in the quality of responses in open-source LLM compared to closed-source models become very obvious, especially when better reasoning capabilities are necessary for the task. The capability to follow general instructions, as well as closed-source models, is definitely not as good.

For example, Retrieval Augmented Generation (RAG) LLM applications encompass many demanding tasks that require enhanced reasoning abilities. The team at LlamaIndex has done a commendable job scoping out various tasks needed in an RAG application. However, their prompt engineering efforts have primarily focused on closed-source models, such as OpenAI GPT-4 and GPT-3.5.

The LlamaIndex team was generous enough to… Read the full blog for free on Medium.

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Published via Towards AI