Prompting

When you first start using Mistral models, your initial interaction will revolve around prompts. Mastering the art of crafting effective prompts is essential for generating high-quality responses from Mistral models or other LLMs.

Main Concepts

The Art of Crafting Prompts

Below, we cover the core concepts of prompting and how to craft effective prompts to maximize the capabilities of our models.

System Prompt

When providing instructions, there are two levels of input you can give the model: system and user.

The system prompt is provided at the beginning of the conversation. It sets the general context and instructions for the model’s behavior and is typically managed by the developer.
The user prompt is provided during the conversation to give the model specific context or instructions for the current interaction.

note

As a developer, you can still use user prompts to provide additional context or instructions during the conversation if needed. If you cannot control the system prompt, you can still include the general context and instructions in the user prompt by concatenating them with the actual query. Role-Separated Example:

{
    "role": "system",
    "content": "system_prompt"
},
{
    "role": "user",
    "content": "user_prompt"
}

{
    "role": "system",
    "content": "system_prompt"
},
{
    "role": "user",
    "content": "user_prompt"
}

Concatenated Example:

{
    "role": "user",
    "content": "system_prompt\n\nUser: user_prompt"
}

{
    "role": "user",
    "content": "system_prompt\n\nUser: user_prompt"
}

Providing a Purpose

Also called Roleplaying, it's the first step in crafting a prompt and corresponds to defining a clear purpose. A common approach is to start with a concise role and task definition, such as: "You are a <role>, your task is to <task>." This simple yet powerful technique helps steer the model toward a specific vertical and task, ensuring it quickly understands the context and expected output.

Structure

When giving instructions, organize them hierarchically or with a clear structure, such as dividing them into clear sections and subsections. The prompt should be clear and complete. A useful rule of thumb is to imagine you’re writing for someone with no prior context—they should be able to understand and execute the task solely by reading the prompt. Example of a Well-Structured Prompt:

You are a language detection model, your task is to detect the language of the given text.
# Available Languages
Select the language from the following list:
- English: "en"
- French: "fr"
- Spanish: "es"
- German: "de"
Any language not listed must be classified as "other" with the code "on".
# Response Format
Your answer must follow this format:
{"language_iso": <language_code>}
# Examples
Below are sample inputs and expected outputs:
## English
User: Hello, how are you?
Answer: {"language_iso": "en"}
## French
User: Bonjour, comment allez-vous?
Answer: {"language_iso": "fr"}

You are a language detection model, your task is to detect the language of the given text.
# Available Languages
Select the language from the following list:
- English: "en"
- French: "fr"
- Spanish: "es"
- German: "de"
Any language not listed must be classified as "other" with the code "on".
# Response Format
Your answer must follow this format:
{"language_iso": <language_code>}
# Examples
Below are sample inputs and expected outputs:
## English
User: Hello, how are you?
Answer: {"language_iso": "en"}
## French
User: Bonjour, comment allez-vous?
Answer: {"language_iso": "fr"}

Formatting

Formatting is critical for crafting effective prompts. It allows you to explicitly highlight different sections, making the structure intuitive for both the model and developers. Markdown and/or XML-style tags are ideal because they are:

Readable: Easy for humans to scan.
Parsable: Simple to extract information programmatically.
Familiar: Likely seen massively during the model’s training. Good formatting not only helps the model understand the prompt but also makes it easier for developers to iterate and maintain the application.

Example Prompting

Example prompting is a technique where you provide a few task examples to improve the model’s understanding, accuracy and specially the output format.

Few-Shot Prompting

A specific example of this is few-shot prompting, where artificial interactions between the user and model are included in the conversation history. In contrast, zero-shot prompting involves no examples. Direct Example in a Prompt:

[...]
# Examples
Input: Hello, how are you?
Output: {"language_iso": "en"}
[...]

[...]
# Examples
Input: Hello, how are you?
Output: {"language_iso": "en"}
[...]

Standard Few-Shot Prompting Structure:

[
    {
        "role": "system",
        "content": "You are a language detection model. Your task is to detect the language of the given text.\n[...]"
    },
    {
        "role": "user",
        "content": "Hello, how are you?"
    },
    {
        "role": "assistant",
        "content": "{\"language_iso\": \"en\"}"
    },
    {
        "role": "user",
        "content": "Bonjour, comment allez-vous?"
    },
    {
        "role": "assistant",
        "content": "{\"language_iso\": \"fr\"}"
    }
]

[
    {
        "role": "system",
        "content": "You are a language detection model. Your task is to detect the language of the given text.\n[...]"
    },
    {
        "role": "user",
        "content": "Hello, how are you?"
    },
    {
        "role": "assistant",
        "content": "{\"language_iso\": \"en\"}"
    },
    {
        "role": "user",
        "content": "Bonjour, comment allez-vous?"
    },
    {
        "role": "assistant",
        "content": "{\"language_iso\": \"fr\"}"
    }
]

Structured Outputs

With your prompt ready, you can now focus on the output. To ensure the model generates structured and predictable responses, we provide the ability of enforcing a specific JSON output format. This is particularly useful for tasks requiring a consistent structure that can be easily parsed and processed programmatically. If used in the example above, this technique would ensure that the model’s responses are consistent in terms of formating and also allows to enforce the categories to be used. If you are interested, for more details on how to use structured outputs, you can refer to the Structured Outputs docs.

Advice

When building a prompt, it is important to stay flexible and experiment, different models from different labs, and even a simple update, can change the model behaviour and a consistent prompt may be impacted by these changes. Hence, do not hesitate to revisit your prompts and see the impact, similar to how you would iterate on your code and model training, you should iterate on your prompts and evaluate the impact of your changes.

What to Avoid

What you should Avoid

Below we provide a list of "good to know" advice about what to avoid doing. The list is not exhaustive and can depend on your use case - but these points are good to keep in mind while building your prompts.

Avoid Subjective and Blurry Words

Avoid blurry quantitative adjectives: “too long”, “too short”, “many”, “few”, etc.
- Instead, provide objective measures.
Avoid blurry words like “things”, “stuff”, “write an interesting report”, “make it better”, etc.
- Instead, state exactly what you mean by “interesting”, “better”, etc.

Avoid Contradictions

As your system prompt gets long, slight contradictions may appear.
Example:

“If the new data is related to an existing database record, update this record.”
“If the data is new, create a new record.”
This is unclear because new data could either update an existing record or create a new one.

Instead, use a decision tree:

## How to update database records
Follow these steps:
- If the data does not include new information (i.e., it already exists in a record):
    - Ignore this data.
- Otherwise, if the data is not related to any existing record in the same table:
    - Create a new record.
- Otherwise, if the related record is larger than 100 characters:
    - Create a new record.
- Otherwise, if the data directly contradicts the existing record:
    - Delete the existing record and create a new one.
- Otherwise:
    - Update the existing record to include the new data.

## How to update database records
Follow these steps:
- If the data does not include new information (i.e., it already exists in a record):
    - Ignore this data.
- Otherwise, if the data is not related to any existing record in the same table:
    - Create a new record.
- Otherwise, if the related record is larger than 100 characters:
    - Create a new record.
- Otherwise, if the data directly contradicts the existing record:
    - Delete the existing record and create a new one.
- Otherwise:
    - Update the existing record to include the new data.

Do Not Make LLMs Count Words

Avoid: “If the record is too long, split it into multiple records.”
Avoid: “If the record is longer than 100 characters, split it into multiple records.”

Instead, provide character counts as input:

Existing records:
  - { record: “User: Alice, Age: 30”, charCount: 15 }
  - { record: “User: Bob, Age: 25”, charCount: 13 }
New data:
  - { data: “User: Charlie, Age: 35”, charCount: 17 }

Existing records:
  - { record: “User: Alice, Age: 30”, charCount: 15 }
  - { record: “User: Bob, Age: 25”, charCount: 13 }
New data:
  - { data: “User: Charlie, Age: 35”, charCount: 17 }

Do Not Generate Too Many Tokens

Models are faster at ingesting tokens than generating them. If using structured outputs, only ask the model to generate what is strictly necessary.
Bad Examples:

Generating full record content for a NO_OP operation.
Generating an entire book in one shot.

Only generate the update or necessary data.

Prefer Worded Scales

If you need a model to rate something, use a worded scale for better performance.
Avoid:

“Rate these options on a 1 to 5 scale, 1 being highly irrelevant and 5 being highly relevant.”

“Rate these options on a 1 to 5 scale, 1 being highly irrelevant and 5 being highly relevant.”

Use:

Rate these options using this scale:
- Very Low: if the option is highly irrelevant
- Low: if the option is not good enough
- Neutral: if the option is not particularly interesting
- Good: if the option is worth considering
- Very Good: for highly relevant options

Rate these options using this scale:
- Very Low: if the option is highly irrelevant
- Low: if the option is not good enough
- Neutral: if the option is not particularly interesting
- Good: if the option is worth considering
- Very Good: for highly relevant options

You can convert this worded scale to a numeric one if needed.

Prompting Examples

Below we walk you through example prompts showing four different prompting capabilities:

Classification
Summarization
Personalization
Evaluation

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