GitHub Copilot Prompt for Comparing Offers
GitHub Copilot, the AI-powered coding assistant, is not limited to code generation. It also excels in structured data analysis, particularly comparing commercial, technical, or pricing offers. By leveraging its reasoning and formatting capabilities, you can transform a tedious task into a methodical and repeatable process. Whether you are comparing vendor offers, SaaS subscriptions, business proposals, or technical quotes, GitHub Copilot helps you structure your criteria, weight decision factors, and produce a clear summary. 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 a thorough analysis without missing criteria, while making the result easily shareable with your team. Discover how to craft the ideal prompt to get precise, structured, and actionable offer comparisons directly from your IDE.
Paste in your AI
Paste this prompt in ChatGPT, Claude or Gemini and customize the variables in brackets.
Compare the following offers using a structured analysis grid. For each offer, evaluate the criteria below on a scale of 1 to 5, then generate a Markdown comparison 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 weights):
- Price / value for money (weight: 30%)
- Included features (weight: 25%)
- Support and assistance (weight: 15%)
- Scalability and evolvability (weight: 15%)
- Contractual terms and flexibility (weight: 15%)
Expected output format:
- Comparison table with scores per criterion
- Calculation of total weighted score for each offer
- Analysis of strengths and weaknesses for each offer (max 3 points per offer)
- Final reasoned recommendation based on usage profile
- Points of vigilance and questions to ask before signing
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Why this prompt works
This prompt works thanks to a layered structure: the weighted criteria force a systematic rather than intuitive evaluation, eliminating confirmation biases. The explicit output format guides the model toward a comprehensive and usable response, while the request for points of vigilance pushes the analysis beyond simple surface-level comparison.
Use Cases
Variants
Expected Output
You will get a complete Markdown table with scores per criterion, calculated weighted scores, and a clear ranking of offers. The output will also include a qualitative analysis of each offer's strengths and weaknesses, a reasoned recommendation suited to your context, and a list of questions to ask vendors before any decision.
Frequently Asked Questions
Can GitHub Copilot compare proposals from PDF documents or screenshots?
GitHub Copilot primarily works with text within your IDE. To compare proposals from PDFs, you first need to extract the 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 launching the comparison.
How do I adapt the comparison criteria to my industry?
Replace the generic criteria in the prompt with those specific to your field. For example, for cloud service proposals, add latency, security certifications, and data location. For service proposals, include delivery times, contractual penalties, and customer references. The key is to always assign a weight to each criterion to reflect your actual priorities.
Can recurring proposal comparisons be automated with GitHub Copilot?
Yes, by creating a script template within your project. Define a data structure (JSON or YAML) to input the proposals, and a standardized Copilot prompt that reads this structure to generate the comparison. This way, for each new batch of proposals, you simply update the data and rerun the prompt. You can even version your comparisons in Git to keep a decision history.
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Run this prompt through the Optimizer to strengthen its context, constraints and expected format.
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- LĂ©aAI
Pour un résultat plus fiable, remplacez les placeholders par des données précises (prix exacts, limites d’usage, durées d’engagement). Ajoutez aussi votre profil d’utilisation dans la recommandation finale (ex. petite équipe, forte croissance). Si besoin, demandez un tableau récapitulatif des pondérations modifiables pour tester différents scénarios.
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