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Prompt Engineering Guide · Introduction
General Tips for Designing Prompts
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 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. Iterating 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.
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 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:
Prompt:
### Instruction ###
Translate the text below to Spanish:
Text: "hello!"
Output:
¡Hola!
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 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.
Prompt:
Extract the name of places in the following text.
Desired format:
Place: <comma_separated_list_of_places>
Input: "Although these developments are encouraging to researchers, much is still a mystery. “We often have a black box between the brain and the effect we see in the periphery,” says Henrique Veiga-Fernandes, a neuroimmunologist at the Champalimaud Centre for the Unknown in Lisbon. “If we want to use it in the therapeutic context, we actually need to understand the mechanism.“"
Output:
Place: Champalimaud Centre for the Unknown, Lisbon
Input text is obtained from this Nature article.
Avoid Impreciseness
Given the tips above about being detailed and improving format, it's easy to fall into the trap of wanting to be too clever about prompts and potentially creating imprecise descriptions. It's often better to be specific and direct. The analogy here is very similar to effective communication -- the more direct, the more effective the message gets across.
For example, you might be interested in learning the concept of prompt engineering. You might try something like:
Explain the concept prompt engineering. Keep the explanation short, only a few sentences, and don't be too descriptive.
It's not clear from the prompt above how many sentences to use and what style. You might still somewhat get good responses with the above prompts but the better prompt would be one that is very specific, concise, and to the point. Something like:
Use 2-3 sentences to explain the concept of prompt engineering to a high school student.
To do or not to do?
Another common tip when designing prompts is to avoid saying what not to do but say what to do instead. This encourages more specificity and focuses on the details that lead to good responses from the model.
Here is an example of a movie recommendation chatbot failing at exactly what I don't want it to do because of how I wrote the instruction -- focusing on what not to do.
Prompt:
The following is an agent that recommends movies to a customer. DO NOT ASK FOR INTERESTS. DO NOT ASK FOR PERSONAL INFORMATION.
Customer: Please recommend a movie based on my interests.
Agent:
Output:
Sure, I can recommend a movie based on your interests. What kind of movie would you like to watch? Do you prefer action, comedy, romance, or something else?
Here is a better prompt:
Prompt:
The following is an agent that recommends movies to a customer. The agent is responsible to recommend a movie from the top global trending movies. It should refrain from asking users for their preferences and avoid asking for personal information. If the agent doesn't have a movie to recommend, it should respond "Sorry, couldn't find a movie to recommend today.".
Customer: Please recommend a movie based on my interests.
Agent:
Output:
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.