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GitHub Copilot Prompt for Comparing Offers

GitHub Copilot, the AI-powered coding assistant, is not limited to code generation. It also excels at structured data analysis, particularly comparing commercial, technical, or pricing offers. By leveraging its reasoning and formatting capabilities, you can turn a tedious task into a methodical and reproducible process. Whether you are comparing vendor offers, SaaS subscriptions, business proposals, or technical quotes, GitHub Copilot helps you structure your criteria, weigh decision factors, and produce a clear synthesis. The tool is especially effective when used in a development environment where comparative data can be processed as tables, JSON objects, or Markdown files. This programmatic approach to comparison ensures comprehensive analysis without missing criteria, while making the result easily shareable with your team. Discover how to formulate the ideal prompt to get precise, structured, and actionable offer comparisons directly from your IDE.

The prompt

GitHub Copilot

Compare the following offers using a structured analysis grid. For each offer, evaluate the criteria below on a scale from 1 to 5, then generate a comparative Markdown table with a final weighted score.

Offers to compare:

  • [Offer A: name, price, main features]
  • [Offer B: name, price, main features]
  • [Offer C: name, price, main features]

Evaluation criteria (with weight):

  1. Price / value for money (weight: 30%)
  2. Included features (weight: 25%)
  3. Support and assistance (weight: 15%)
  4. Scalability and evolvability (weight: 15%)
  5. Contractual conditions and flexibility (weight: 15%)

Expected output format:

  1. Comparative table with scores per criterion
  2. Calculation of the total weighted score for each offer
  3. Analysis of strengths and weaknesses of each offer (max 3 points per offer)
  4. Final reasoned recommendation based on usage profile
  5. Points of attention and questions to ask before signing

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Why it works

This prompt works because of a layered structure: the weighted criteria force a systematic rather than intuitive evaluation, eliminating confirmation biases. The explicit output format guides the model towards an exhaustive and actionable response, while the request for points of attention pushes the analysis beyond superficial comparison.

Expected result

You will get a complete Markdown table with scores per criterion, calculated weighted scores, and a clear ranking of the offers. The output will also include a qualitative analysis of each offer's strengths and weaknesses, a reasoned recommendation tailored to your context, and a list of questions to ask vendors before any decision.

Variants by level

FAQ

Can GitHub Copilot compare offers from PDF documents or screenshots?
GitHub Copilot primarily works with text in your IDE. To compare offers from PDFs, you first need to extract key information and paste it into a file (Markdown, JSON, or plain text) in your editor. You can structure the data as a table or object, then ask Copilot to analyze it. For screenshots, transcribe the essential elements into text before starting the comparison.
How can I adapt the comparison criteria to my business sector?
Replace the generic criteria in the prompt with those specific to your domain. For example, for cloud offers, add latency, security certifications, and data location. For service offers, include delivery times, contractual penalties, and client references. The key is to always assign a weight to each criterion to reflect your actual priorities.
Can I automate recurring offer comparisons with GitHub Copilot?
Yes, by creating a template script in your project. Define a data structure (JSON or YAML) to input the offers, and a standardized Copilot prompt that reads this structure to generate the comparison. Thus, for each new set of offers, you just update the data and rerun the prompt. You can even version your comparisons in Git to keep a history of decisions.

How to use this prompt

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

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