Brex’s Prompt Engineering Guide (github.com)

Article summary

Brex's Prompt Engineering Guide is a document created by Brex for internal purposes, covering the history of large language models, strategies, guidelines, and safety recommendations for working with and building programmatic systems on top of large language models. The guide discusses the importance of prompt engineering and provides examples and best practices for crafting effective prompts. The document is a living document, with the state-of-the-art best practices and strategies around large language models evolving rapidly every day. The guide also touches on the potential applications of large language models in various industries, including finance and technology.

Main themes

• large language models
• prompt engineering
• natural language processing
• AI applications
• industry trends
• technology investment
• marketing and branding
• language and linguistics
• computer science and coding

What commenters say

• The field of prompt engineering may not require a strong background in English or linguistics, as large language models can understand and generate text based on simple prompts.
• It is easier to teach a computer science major to write passable English than it is to teach an English major to write passable code.
• The idea that prompt engineering is a valuable skill that will be in high demand in the future is not universally accepted, with some commenters arguing that the field may become automated or less relevant as large language models improve.
• The use of JSON versus YAML for communicating data structure to large language models is a topic of debate, with some arguing that YAML is more effective while others prefer JSON.
• Brex's interest in large language models and prompt engineering may be driven by a desire to use these technologies to automate internal processes and improve their products and services.
• The release of Brex's Prompt Engineering Guide may be seen as a marketing move, but it also reflects the company's genuine interest in and use of large language models.
• The potential applications of large language models in finance and other industries are significant, and companies like Brex are likely to continue exploring and investing in these technologies.
• The importance of understanding the limitations and potential biases of large language models is a key theme in the discussion, with some commenters highlighting the need for careful evaluation and testing of these models.