# Mistral Models Prompt Guide **Table of Contents** 1. [Give the Model a Clear Purpose](https://app.gitbook.com/o/wESD8a1fR7H7ddwkYO61/s/IabFtGTeQzwfWCzp8vd6/~/edit/~/changes/458/for-users/prompt-writing-guide/mistral-models-prompt-guide#id-1-give-the-model-a-clear-purpose) 2. [Structure Prompts Clearly](https://app.gitbook.com/o/wESD8a1fR7H7ddwkYO61/s/IabFtGTeQzwfWCzp8vd6/~/edit/~/changes/458/for-users/prompt-writing-guide/mistral-models-prompt-guide#id-2-structure-prompts-clearly) 3. [Use Formatting](https://app.gitbook.com/o/wESD8a1fR7H7ddwkYO61/s/IabFtGTeQzwfWCzp8vd6/~/edit/~/changes/458/for-users/prompt-writing-guide/mistral-models-prompt-guide#id-3-use-formatting) 4. [Add examples when Accuracy or Format Matters](https://app.gitbook.com/o/wESD8a1fR7H7ddwkYO61/s/IabFtGTeQzwfWCzp8vd6/~/edit/~/changes/458/for-users/prompt-writing-guide/mistral-models-prompt-guide#id-4-add-examples-when-accuracy-or-format-matters) 5. [Don't Overuse Reasoning](https://app.gitbook.com/o/wESD8a1fR7H7ddwkYO61/s/IabFtGTeQzwfWCzp8vd6/~/edit/~/changes/458/for-users/prompt-writing-guide/mistral-models-prompt-guide#id-5-dont-overuse-reasoning) 6. [Avoid Subjective Words](https://app.gitbook.com/o/wESD8a1fR7H7ddwkYO61/s/IabFtGTeQzwfWCzp8vd6/~/edit/~/changes/458/for-users/prompt-writing-guide/mistral-models-prompt-guide#id-6-avoid-subjective-words) 7. [Avoid Self Count of Words or Characters](https://app.gitbook.com/o/wESD8a1fR7H7ddwkYO61/s/IabFtGTeQzwfWCzp8vd6/~/edit/~/changes/458/for-users/prompt-writing-guide/mistral-models-prompt-guide#id-7-avoid-self-count-of-words-or-characters) 8. [Use Worded Scales over Numeric Scales](https://app.gitbook.com/o/wESD8a1fR7H7ddwkYO61/s/IabFtGTeQzwfWCzp8vd6/~/edit/~/changes/458/for-users/prompt-writing-guide/mistral-models-prompt-guide#id-8-use-worded-scales-over-numeric-scales) 9. [Prompt Templates](https://app.gitbook.com/o/wESD8a1fR7H7ddwkYO61/s/IabFtGTeQzwfWCzp8vd6/~/edit/~/changes/458/for-users/prompt-writing-guide/mistral-models-prompt-guide#id-9-prompt-templates) *** ### 1. Give the Model a Clear Purpose Mistral recommends defining a clear purpose early, often through a simple role-and-task setup like: **“You are a \, your task is to \.”** **Less Effective** ``` Analyze this customer feedback. ``` **More Effective** ``` You are a customer insights analyst. Your task is to review this customer feedback and identify the top 3 recurring complaints. ``` *Tip: Lead with role + task before adding detailed instructions.* *** ### 2) Structure Prompts Clearly Mistral explicitly recommends organizing prompts hierarchically with clear sections and subsections. A good structure often includes: * Context * Task * Instructions or steps * Constraints * Output format **Unclear Prompt** ``` Review this text and tell me what matters. ``` **Clear Prompt** ``` Context: Internal Q2 review for non-technical executives Task: Summarize the document Instructions: Identify the 3 most important insights Remove technical jargon Note any major risks or opportunities Constraints: Keep under 200 words Output Format: 3 bullets only ``` *** ### 3. Use Formatting Formatting is a core prompting tool. It's best to use structures such as markdown and/or XML-style tags because they are readable, parsable, and familiar to models. **Recommended formats:** * Markdown headings * Bullets and numbered steps * XML-style sections like ``, ``, `` **Example** ``` This will be used in a board presentation. Summarize the report for executives. - Use plain language - Keep under 120 words - Output only bullet points ``` *Tip: The more complex the task, the more important formatting becomes.* *** ### 4. Add examples when Accuracy or Format Matters Use example prompting, especially few-shot prompting, when you want better accuracy or tighter control over output format. **This is especially useful for:** * classification * extraction * tagging * JSON outputs * repeated formatting tasks ``` # Task Detect the language of the input text. # Examples Input: Hello, how are you? Output: {"language_iso": "en"} Input: Bonjour, comment allez-vous? Output: {"language_iso": "fr"} ``` **Tip:** If output structure matters, show the model at least one example of the exact format you want. *** ### 5. Don't Overuse Reasoning For simple rewriting, extraction, formatting, or short summaries, a well-structured prompt is usually enough. **Use reasoning for:** * math * coding * multi-step logic * difficult comparisons * edge-case-heavy decisions *** ### 6. Avoid Subjective Words Words like “too long,” “interesting,” “better,” “some,” or “a few” are vague. Mistral recommends replacing them with objective measures. **Instead of:** Make it shorter\ **Use:** Keep it under 120 words *** ### 7. Avoid Self Count of Words or Characters Mistral advises against relying on the model to calculate thresholds itself. If length matters operationally, pass the count in as data instead. *** ### 8. Use Worded Scales over Numeric Scales For evaluations, Mistral recommends labels like Very Low, Low, Neutral, Good, Very Good instead of a raw 1–5 scale. *** ### 9. Prompt Templates #### General Task Template ``` System: You are a [role]. Your task is to [goal]. User: ## Context [Background and audience] ## Task [Exactly what you want done] ## Instructions 1. [Step one] 2. [Step two] 3. [Step three] ## Constraints - [Tone] - [Length] - [What to avoid] ## Output Format [Bullets, table, JSON, paragraph, etc.] ``` #### Few Shot Template ``` System: You are a [role]. Follow the examples exactly. User: ## Task Classify each input into one category. ## Categories - Positive - Neutral - Negative ## Examples Input: "This product is amazing." Output: Positive Input: "It arrived yesterday." Output: Neutral Input: "It broke after one use." Output: Negative ## New Input [Insert text here] ## Output Format Return only the category. ``` #### Structured Output Template ``` System: You are an extraction assistant. User: ## Task Extract the key details from the text. ## Fields - company - product - launch_date - summary ## Constraints - Return valid JSON only - Use null if a field is missing - Do not add extra keys ``` --- # Agent Instructions: Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter: ``` GET https://docs.en.theblockbrain.ai/for-users/prompt-writing-guide/mistral-models-prompt-guide.md?ask= ``` The question should be specific, self-contained, and written in natural language. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.