Mistral Prompt for Analyzing User Feedback

Analyzing user feedback is a strategic lever for any company looking to improve its products and services. With Mistral, the leading French language model, you can automate this analysis at scale while maintaining remarkable interpretative finesse. Whether your feedback comes from satisfaction surveys, online reviews, support tickets, or social media comments, Mistral excels at sentiment detection, identifying recurring themes, and prioritizing pain points. Its native understanding of French allows it to grasp cultural nuances, sarcasm, and idiomatic expressions that other models may miss. By structuring your prompt correctly, you can turn hundreds of raw feedbacks into actionable insights in seconds. This guide provides an optimized prompt for extracting maximum value from your user feedback with Mistral, along with variants suited to your expertise level and data complexity.

The prompt

Mistral

You are a senior UX analyst specializing in leveraging user feedback. I will provide you with a list of raw user feedbacks. For each batch of feedbacks, perform the following analysis:

Sentiment analysis: Classify each feedback as Positive, Negative, Neutral, or Mixed, with a confidence score (0-100%).
Theme extraction: Identify the main themes that emerge (UX/UI, performance, price, customer support, features, onboarding, etc.). Group feedbacks by theme.
Critical pain point detection: List the 5 most mentioned problems by order of frequency and emotional intensity.
Improvement opportunities: For each identified pain point, propose a concrete and prioritized recommendation (impact vs. effort).
Key verbatims: Extract the 3 most representative quotes (positive and negative).
Estimated NPS score: Based on the overall tone of the feedbacks, estimate an NPS range.

Format your response in a structured table for each section. Be factual, precise, and actionable.

Here are the feedbacks to analyze:
[PASTE YOUR FEEDBACKS HERE]

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Why it works

This prompt leverages role-playing (senior UX analyst) to activate a rigorous analytical register in Mistral. The six-step numbered structure forces systematic analysis and avoids superficial responses. The request for confidence scores and impact/effort prioritization pushes the model to produce quantified results directly usable by a product team.

Expected result

You get a structured report including a sentiment classification of each feedback, a thematic mapping of responses, a top 5 of pain points with prioritized recommendations, representative verbatims, and an NPS estimate. Everything is formatted in clear tables, ready to share with your product team or management.

Variants by level

FAQ

How many feedbacks can I analyze in a single request with Mistral?

Mistral Large supports a 128K token context window, allowing you to analyze about 200 to 400 short feedbacks (1-2 sentences) in a single request. For larger volumes, split your feedbacks into batches of 100-200 and request a consolidated summary at the end. Tip: number your feedbacks to make them easier to reference in the analysis.

Is Mistral reliable for detecting sarcasm and nuances in French?

Mistral, developed by a French team, offers one of the best understandings of French on the market. It correctly detects sarcasm in about 80-85% of cases, which is superior to most English-language models. To improve detection, add an explicit instruction in your prompt like 'Be attentive to sarcasm, irony, and familiar French expressions.' Ambiguous cases will be flagged with a lower confidence score.

How can I integrate this analysis into an automated workflow?

You can use the Mistral API (via La Plateforme) to automate the analysis. Connect your feedback source (Typeform, Zendesk, Google Sheets) to a Python script that calls the Mistral API with the optimized prompt, then store the results in a database or dashboard. Add the parameter response_format: {type: 'json_object'} in your API call to get results directly usable programmatically. No-code tools like Make or n8n also offer native Mistral integrations.

How to use this prompt

Copy the prompt with the button above.
Paste it into ChatGPT, Claude or your favorite AI assistant.
Replace the bracketed variables with your details, then refine the result.

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