Prompt Engineering Foundation Notes (Part 1)
This article covers the basics of prompt engineering for large language models (LLMs), including the differences between base and instruction-tuned LLMs, and provides sample code for using the OpenAI API.
Why it matters
Prompt engineering is a crucial skill for developers and researchers working with large language models, as it allows them to extract the desired outputs from these powerful AI systems.
Key Points
- 1There are two main types of LLMs: base models and instruction-tuned models
- 2Base LLMs try to predict the next likely word, while instruction-tuned LLMs focus on following specific instructions
- 3The article introduces helper functions to make it easier to use prompts and view generated outputs
- 4It covers setting up the OpenAI API and using the chat completion endpoint
Details
The article discusses the differences between base LLMs and instruction-tuned LLMs. Base LLMs are trained to predict the next likely word, while instruction-tuned LLMs are focused on generating outputs based on specific instructions. The article provides sample code for setting up the OpenAI API and using the chat completion endpoint to interact with LLMs. It also introduces helper functions to simplify the process of using prompts and viewing generated outputs. The content is intended to provide a foundation for prompt engineering, which is an important skill for effectively using large language models.
No comments yet
Be the first to comment