GitHub Copilot Prompt for Analyzing User Feedback
Analyzing user feedback is a crucial step to improve a product, service, or customer experience. Yet manually processing hundreds or thousands of reviews—whether from reviews, support tickets, NPS forms, or social media comments—represents considerable work. GitHub Copilot, thanks to its natural language understanding capabilities integrated directly into your code editor, allows you to automate this analysis with remarkable accuracy. By formulating suitable prompts, you can ask Copilot to categorize sentiments, extract recurring themes, identify priority friction points, and generate actionable summaries for your product teams. Whether you work with CSV files, JSON exports, or raw data copied from a support tool, Copilot transforms your IDE into a true qualitative analysis platform. This approach is particularly useful for developers and product managers who want to integrate feedback analysis directly into their technical workflow, without relying on expensive third-party tools or advanced data science skills.
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Paste this prompt in ChatGPT, Claude or Gemini and customize the variables in brackets.
Analyze the following user feedback and generate a structured report. For each feedback: 1) Identify the sentiment (positive, negative, neutral, mixed) with a confidence score from 0 to 1. 2) Extract the main themes addressed (UX, performance, pricing, functionality, support, onboarding, etc.). 3) Detect implicit or explicit feature requests. 4) Assess the urgency level (critical, important, minor). Then, produce an overall summary including: sentiment distribution as percentages, top 5 most mentioned themes with their frequency, top 3 priority actions recommended ranked by potential impact, and the most representative verbatims for each category. Format the result in structured JSON with keys: summary, sentiment_distribution, top_themes, priority_actions, feature_requests, and representative_quotes. Here is the feedback to analyze:
[PASTE_YOUR_FEEDBACK_HERE]
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Why this prompt works
This prompt works effectively because it breaks down the analysis into distinct, measurable tasks (sentiment classification, thematic extraction, prioritization), which guides the model toward a structured response rather than a vague summary. The request for confidence scores and JSON format forces Copilot to produce programmatically usable results. Finally, the impact-based prioritization directs toward actionable decisions, turning raw qualitative data into strategic insights.
Use Cases
Variants
Expected Output
You will obtain a structured JSON object containing detailed sentiment distribution, dominant themes ranked by frequency, and a list of priority actions directly usable by your product team. The report will also include the most representative verbatims, facilitating communication of insights to non-technical stakeholders. This format allows direct integration into your dashboards or product tracking tools.
Frequently Asked Questions
How many feedbacks can I analyze at once with GitHub Copilot?
GitHub Copilot is limited by its context window size. In practice, you can analyze between 50 and 150 short feedbacks in a single pass, depending on their length. For larger volumes, it's recommended to split your data into batches of 100 feedbacks and have Copilot generate a per-batch analysis, then consolidate the results into a final summary. You can automate this process by writing a script that iterates over your data and calls Copilot Chat for each batch.
Can GitHub Copilot analyze feedbacks in multiple languages simultaneously?
Yes, GitHub Copilot handles multilingual analysis effectively. It can identify each feedback's language, analyze sentiment, and extract themes regardless of the source language, then produce the summary in your language of choice. For best results, specify the expected languages and the desired report language in your prompt. Note that sentiment analysis accuracy may vary across languages—it's generally better in English and French than in languages less represented in the training data.
How to integrate feedback analysis with Copilot into an automated pipeline?
You can integrate feedback analysis into an automated workflow by using GitHub Copilot Chat in your IDE to generate the code for an analysis script. Ask Copilot to create a Python or TypeScript function that reads your feedbacks from a data source (CSV, API, database), formats them into batches, sends them to a LLM API with the analysis prompt, then stores the structured results. This script can then be integrated into a cron job, a GitHub Action, or a CI/CD pipeline for recurring automated analysis. Copilot is especially good at generating code for parsing and structuring results.
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