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 even thousands of comments—whether from reviews, support tickets, NPS forms, or social media comments—is a considerable task. GitHub Copilot, with its natural language understanding capabilities built directly into your code editor, allows you to automate this analysis with remarkable accuracy. By crafting appropriate 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 turns 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.

The prompt

GitHub Copilot

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, feature, support, onboarding, etc.). 3) Detect implicit or explicit feature requests. 4) Assess the urgency level (critical, important, minor). Then produce a global summary including: sentiment distribution in percentages, top 5 most mentioned themes with their frequency, the 3 recommended priority actions ranked by potential impact, and the most representative verbatims for each category. Format the result as structured JSON with keys: summary, sentiment_distribution, top_themes, priority_actions, feature_requests, and representative_quotes. Here are the feedbacks to analyze:

[PASTE_YOUR_FEEDBACK_HERE]

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Why it works

This prompt works effectively because it breaks down the analysis into distinct and measurable tasks (sentiment classification, thematic extraction, prioritization), guiding the model toward a structured response rather than a vague summary. The request for confidence scores and JSON format forces Copilot to produce results that are programmatically actionable. Finally, impact-based prioritization directly leads to actionable decisions, transforming raw qualitative data into strategic insights.

Expected result

You will obtain a structured JSON object containing the detailed sentiment distribution, dominant themes ranked by frequency, and a list of priority actions directly actionable 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.

Variants by level

FAQ

How many feedback items 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 feedback items in a single pass, depending on their length. For larger volumes, it is recommended to split your data into batches of 100 feedback items and ask Copilot to produce an analysis per batch, 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 feedback in multiple languages simultaneously?

Yes, GitHub Copilot handles multilingual analysis effectively. It can identify the language of each feedback item, analyze sentiment and extract themes regardless of the source language, and then produce the summary in the language of your choice. For best results, specify the expected languages and the desired output language in your prompt. Note that sentiment analysis accuracy may vary by language—it is generally better in English and French than in languages less represented in the training data.

How do I 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 feedback from a data source (CSV, API, database), formats them into batches, sends them to an LLM API with the analysis prompt, and 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 automatic analysis. Copilot excels at generating parsing and result structuring code.

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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