How to prompt Mistral Large

Prompt Mistral Large with compact labeled blocks written as instructions rather than conversation: Role (one line), Task, Context, Constraints and Output format. Use strong action verbs, list constraints as bullets, and skip the pleasantries. Soft phrasing makes the model more likely to suggest than to do.

The prompt structure

Role: Senior Python reviewer.

Task: Find the bugs in the function below and fix them.

Context:
Function parses ISO dates from a CSV export. Some rows have
timezone offsets, some don't.

Constraints:
- Return the corrected function plus a change list.
- Keep the function signature.
- Standard library only.

Output format:
Fenced Python code, then "Bug → Fix" bullets.

For a simple request, Role plus Task is enough. Add the other blocks when the work needs them.

Write system-style, not chat

Write the prompt as instructions the model receives, not as a message to a colleague. Leave out “I was wondering if you could perhaps…”, “please” and “let me know if you need anything else”. Those phrases soften the task, and Mistral answers soft requests with suggestions.

Strong verbs

“Implement”, “fix”, “write”, “summarise”. If you want a deliverable, ask for the deliverable, not a recommendation about it.

Constraints as dense bullets

Mistral handles compact lists well. Put every limit in the Constraints block, and only there. Repeating a limit in both Task and Constraints makes it more likely one copy gets dropped.

Phrase constraints positively where you can, and use placeholders such as [TBD] for missing facts instead of letting the model invent APIs, ticket IDs or audiences.

Name the output shape

Say exactly what comes back: an email with Subject and Body, markdown with H2 sections, or fenced code followed by change bullets. Put language and length in Constraints or Output format.

Keep the role to one line

“Senior reviewer” is enough. A paragraph of persona biography repeats the task and uses up the prompt’s focus.

Why Mistral prompts go wrong

• Long, rambling preambles.
• Soft verbs (“could you look at”).
• No output contract.
• Few-shot samples the model imitates instead of doing your task.
• XML tags it may echo back.
• Unrequested “think step by step” slogans on simple tasks.