OpenAI Unveils Prompt Engineering Guide With Six Strategies for Optimizing GPT-4 Performance

by Alisa Davidson

Published: December 18, 2023 at 7:01 am Updated: December 18, 2023 at 7:01 am

by Victor Dey

Edited and fact-checked:
December 18, 2023 at 7:01 am

In Brief

OpenAI released Prompt Engineering guide for GPT-4, providing detailed insights into ways to enhance LLMs’ efficiency.

The artificial intelligence research organization OpenAI, released its Prompt Engineering guide for GPT-4. The guide offers detailed insights into optimizing the efficiency of Language Models (LLMs).

The guide outlines strategies and tactics that can be combined for greater effectiveness and includes example prompts, offering six key strategies to help users maximize the efficiency of the model.

Clear Instructions

LLM models lack intuition. If outputs are too extensive or simplistic, users should request brief or expert-level responses. The more explicit the user’s instructions, the greater the likelihood of obtaining the desired result.

Provide Reference Texts

Language models may generate inaccurate responses, especially on obscure topics or when asked for citations and URLs. Similar to how notes assist a student, providing reference text can enhance the model’s accuracy. Users can instruct the model to answer using reference text or provide citations from it.

Breakdown the Complex Task into Simpler Instructions

Users should break down a complex system into modular components for improved performance. Complex tasks often have higher error rates than simpler ones. Moreover, complex tasks can be redefined as workflows of simpler tasks, where outputs from earlier tasks construct inputs for later ones.

The Model Requires Time for Analysis

LLM models are more prone to reasoning errors when providing immediate responses. Requesting a “chain of thought” before receiving an answer can help the model reason its way toward more reliable and accurate responses.

Users Should Utilize External Tools

Offset the model’s limitations by providing outputs from other tools. A code execution engine, like OpenAI’s Code Interpreter, can assist in mathematical calculations and code execution. If a task can be done more reliably or efficiently using a tool, consider offloading it for better results.

Test Changes Systematically

Enhancing performance is possible by quantifying it. While altering a prompt may improve performance in specific instances, it could lead to decreased overall performance. To ensure a change positively contributes to performance, establishing a comprehensive test suite may be essential.

By leveraging the Prompt Engineering guide for GPT-4, users can enhance the efficiency of LLMs through explicit methods and tactics ensuring its optimal performance in diverse scenarios.

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About The Author

Alisa is a reporter for the Metaverse Post. She focuses on investments, AI, metaverse, and everything related to Web3. Alisa has a degree in Business of Art and expertise in Art & Tech. She has developed her passion for journalism through writing for VCs, notable crypto projects, and engagement with scientific writing.

Alisa Davidson