Archives/Prompt-Engineering-Guide
2
0
Fork 0
mirror of https://github.com/dair-ai/Prompt-Engineering-Guide synced 2024-11-02 15:40:13 +00:00
Code Issues Packages Projects Releases Wiki Activity

Update tips.en.mdx

Browse Source 
Editing for clarity. Changed passive voice to positive voice.

One thing that is not clear to me is about this sentence: "Versioning your prompt along the way ..."   Do you mean "Iterating your prompt along the way ..."? Versioning sounded like to me is a technical term for version control like Git.
This commit is contained in:
Tao Li 2023-04-16 16:26:49 -07:00 committed by GitHub
parent a050c9a5f5
commit b5f6c22663
No known key found for this signature in database
GPG Key ID: 4AEE18F83AFDEB23
1 changed files with 6 additions and 6 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

12
pages/introduction/tips.en.mdx
View File

@ -4,18 +4,18 @@ Here are some tips to keep in mind while you are designing your prompts:
### Start Simple
As you get started with designing prompts, you should keep in mind that it is really an iterative process that requires a lot of experimentation to get optimal results. Using a simple playground like OpenAI or Cohere's is a good starting point.
As you get started with designing prompts, you should keep in mind that it is really an iterative process that requires a lot of experimentation to get optimal results. Using a simple playground from OpenAI or Cohere is a good starting point.
You can start with simple prompts and keep adding more elements and context as you aim for better results. Versioning your prompt along the way is vital for this reason. As we read the guide you will see many examples where specificity, simplicity, and conciseness will often give you better results.
You can start with simple prompts and keep adding more elements and context as you aim for better results. Versioning your prompt along the way is vital for this reason. As you read the guide, you will see many examples where specificity, simplicity, and conciseness will often give you better results.
When you have a big task that involves many different subtasks, you can try to break down the task into simpler subtasks and keep building up as you get better results. This avoids adding too much complexity to the prompt design process at the beginning.
### The Instruction
You can design effective prompts for various simple tasks by using commands to instruct the model what you want to achieve such as "Write", "Classify", "Summarize", "Translate", "Order", etc.
You can design effective prompts for various simple tasks by using commands to instruct the model what you want to achieve, such as "Write", "Classify", "Summarize", "Translate", "Order", etc.
Keep in mind that you also need to experiment a lot to see what works best. Try different instructions with different keywords, contexts, and data and see what works best for your particular use case and task. Usually, the more specific and relevant the context is to the task you are trying to perform, the better. We will touch on the importance of sampling and adding more context in the upcoming guides.
Others recommend that instructions are placed at the beginning of the prompt. It's also recommended that some clear separator like "###" is used to separate the instruction and context.
Others recommend that you place instructions at the beginning of the prompt. Another recommendation is to use some clear separator like "###" to separate the instruction and context.
For instance:
@ -35,7 +35,7 @@ Text: "hello!"
### Specificity
Be very specific about the instruction and task you want the model to perform. The more descriptive and detailed the prompt is, the better the results. This is particularly important when you have a desired outcome or style of generation you are seeking. There aren't specific tokens or keywords that lead to better results. It's more important to have a good format and descriptive prompt. In fact, providing examples in the prompt is very effective to get desired output in specific formats.
When designing prompts you should also keep in mind the length of the prompt as there are limitations regarding how long this can be. Thinking about how specific and detailed you should be is something to consider. Including too many unnecessary details is not necessarily a good approach. The details should be relevant and contribute to the task at hand. This is something you will need to experiment with a lot. We encourage a lot of experimentation and iteration to optimize prompts for your applications.
When designing prompts, you should also keep in mind the length of the prompt as there are limitations regarding how long the prompt can be. Thinking about how specific and detailed you should be. Including too many unnecessary details is not necessarily a good approach. The details should be relevant and contribute to the task at hand. This is something you will need to experiment with a lot. We encourage a lot of experimentation and iteration to optimize prompts for your applications.
As an example, let's try a simple prompt to extract specific information from a piece of text.
@ -105,4 +105,4 @@ Agent:
Sorry, I don't have any information about your interests. However, here's a list of the top global trending movies right now: [list of movies]. I hope you find something you like!
```
Some of the examples above were adopted from the ["Best practices for prompt engineering with OpenAI API" article.](https://help.openai.com/en/articles/6654000-best-practices-for-prompt-engineering-with-openai-api)
Some of the examples above were adopted from the ["Best practices for prompt engineering with OpenAI API" article.](https://help.openai.com/en/articles/6654000-best-practices-for-prompt-engineering-with-openai-api)