Prompt-Engineering-Guide/pages/introduction/tips.fr.mdx

# 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 like OpenAI or Cohere's 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.

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 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.

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 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.

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_company_names>

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](https://www.nature.com/articles/d41586-023-00509-z).

### 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:*
```
Bien sûr, je peux vous recommander un film en fonction de vos centres d'intérêt. Quel genre de film aimeriez-vous regarder ? Préférez-vous les films d'action, de comédie, romantiques ou autre chose ?
```

Voici une meilleure prompt:

*Prompt:*
```
Le suivant est un agent qui recommande des films à un client. L'agent est responsable de recommander un film parmi les films tendances mondiaux les plus populaires. Il doit éviter de demander aux utilisateurs leurs préférences et de demander des informations personnelles. Si l'agent n'a pas de film à recommander, il doit répondre "Désolé, je n'ai pas pu trouver de film à recommander aujourd'hu

Client : Recommander un film en fonction de mes centres d'intérêt.
Agent:
```

*Output:*
```
Désolé, je n'ai pas d'informations sur vos préférences. Cependant, voici une liste des films les plus populaires dans le monde en ce moment : [liste de films]. J'espère que vous y trouverez quelque chose qui vous plaira !
```

Certains des exemples ci-dessus ont été adoptés à partir de l'article ["Best practices for prompt engineering with OpenAI API" article.](https://help.openai.com/en/articles/6654000-best-practices-for-prompt-engineering-with-openai-api)