GitHub Copilot Prompt to Write a Cover Letter
GitHub Copilot, primarily known as a development assistant, also proves to be a formidable tool for writing professional documents like cover letters. Thanks to its ability to understand context and generate structured text, Copilot can help you produce a compelling letter in minutes. Whether you are applying for a technical position or not, the AI guides you in articulating your skills, background, and motivation convincingly. Copilot's advantage lies in its fine understanding of textual structures: it knows how to organize an argument, adapt the tone to the target industry, and highlight the key elements of your application. By providing a well-constructed prompt with relevant information—target position, company, skills, experiences—you get a personalized letter that far exceeds generic templates. This approach saves you considerable time while maintaining a professional quality level, allowing you to focus on final personalization rather than writing from scratch.
Paste in your AI
Paste this prompt in ChatGPT, Claude or Gemini and customize the variables in brackets.
Write a professional cover letter for the position of [JOB_TITLE] at [COMPANY_NAME]. Here is my profile: I am [YOUR_PROFILE: degree, years of experience, field]. My key skills are: [LIST_OF_3-5_SKILLS]. My most relevant experience: [briefly describe a major achievement with a quantified result]. What attracts me to this company: [specific reason related to the company]. The letter must include a compelling hook, a paragraph on my background and skills relevant to the position, a paragraph showing my knowledge of the company and specific motivation, and a conclusion with a call to action for an interview. Adopt a professional but authentic tone, avoid clichés like 'dynamic and motivated', and prioritize concrete evidence over vague statements. The letter should fit on one page maximum.
Personalize this prompt with Léa
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Why this prompt works
This prompt works thanks to the contextual role technique combined with precise structural instructions. By providing concrete data (numbers, skills, achievements), the generation is anchored in reality and generic content is avoided. Negative constraints ('avoid clichés') and the imposed structure (hook, background, motivation, conclusion) guide the model towards a professional and differentiating result.
Use Cases
Variants
Expected Output
You get a cover letter structured in four parts, personalized with your real information and tailored to the target position. The tone is professional without being rigid, arguments are backed by concrete evidence, and the letter respects the one-page format while standing out from generic applications.
Frequently Asked Questions
Can GitHub Copilot really write a professional-quality cover letter?
Yes, GitHub Copilot excels at generating structured text when given precise context. The key is to feed it your real information (experiences, skills, quantified achievements) rather than asking it to make things up. The result always requires proofreading and personal adjustments, but it provides a solid foundation that saves you 70 to 80% of the writing time. Think of Copilot as a smart first draft that you then refine.
How do I personalize the generated letter so it doesn't sound like a template?
Three strategies work particularly well: first, include very specific details about the company in your prompt (a recent project, a value that resonates with you, an article you've read). Second, mention an anecdote or a unique quantified result from your background. Third, ask Copilot to rephrase by specifying the desired tone (for example, 'tone of a pragmatic engineer' rather than just 'professional'). The more specific your inputs, the more unique and authentic the output will be.
Should I use Copilot in a code editor or via Copilot Chat?
Both approaches work. Copilot Chat (integrated into VS Code or on github.com) is the most convenient for this type of task: you paste your prompt directly into the chat window and get the letter in return. You can then iterate by requesting changes ('shorten the second paragraph', 'strengthen the opening hook', 'adjust the tone for a startup'). The advantage of chat is the ability to have a back-and-forth to refine the result until you're completely satisfied.
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Run this prompt through the Optimizer to strengthen its context, constraints and expected format.
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- LéaAI
Pour des résultats plus ciblés, collez l'intégralité de l'offre d'emploi dans le prompt : Copilot alignera vos compétences sur les exigences exactes du poste. Ensuite, générez deux variantes — l'une conservative, l'autre plus audacieuse — en modifiant la phrase sur le ton. Enfin, relisez à voix haute et remplacez toute formule que vous n'écririez jamais ; le modèle reproduit vos tournures, pas votre voix.
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