Content by randy.pagels@xebia.com (randy pagels) (47)

Randy Pagels shares practical tips for developers to maximize GitHub Copilot's effectiveness by providing better context and intent, rather than relying on longer prompts.

Randy Pagels shares practical guidance for developers on building confidence in GitHub Copilot’s coding output by combining smart trust with targeted verification.

Randy Pagels outlines actionable strategies for reviewing GitHub Copilot's code output with a senior developer's mindset, emphasizing intent, assumptions, and iterative improvement.

Randy Pagels discusses effective strategies for balancing human judgment and automation through GitHub Copilot, highlighting when to take the lead and when to delegate to maximize development workflow efficiency.

Randy Pagels shares practical strategies for treating GitHub Copilot as a true coding teammate in 2026, highlighting collaboration, feedback, and thoughtful interaction as ways to unlock Copilot's full potential.

Randy Pagels recaps four practical prompt patterns for GitHub Copilot—Persona, Reflection, Refusal Breaker, and Cognitive Verifier—offering actionable techniques to help developers steer Copilot towards more relevant, clear, and effective results.

Randy Pagels presents actionable recipes for using the Cognitive Verifier Pattern with GitHub Copilot, showing developers how to prompt Copilot to ask clarifying questions for cleaner, more accurate answers.

Randy Pagels explains the Refusal Breaker Pattern, showing how developers can reframe prompts for GitHub Copilot when it declines requests, ensuring helpful and compliant AI assistance.

Randy Pagels shares practical advice on the Reflection Pattern, guiding developers on how to prompt GitHub Copilot to explain its code suggestions and logic before accepting or revising them.

Randy Pagels shares actionable strategies for guiding GitHub Copilot using the Persona Pattern, helping developers customize Copilot's role and improve answer quality.

Randy Pagels explains why developers should manage AI prompts like code—using Git, code reviews, and clear documentation for continuous improvement.

Randy Pagels explains how to provide optimal code context to GitHub Copilot for effective assistance, offering practical tips for each Copilot feature.

Randy Pagels shows how to supercharge developer workflows by using .prompt.md files with GitHub Copilot, making prompts reusable and coding more efficient.

Randy Pagels explains how to use the copilot-instructions.md file to set project-wide rules for GitHub Copilot, helping teams achieve consistency and safer code generation.

Randy Pagels delivers a developer-focused guide on how to select the right AI model in GitHub Copilot, helping users weigh speed versus reasoning to improve coding efficiency.

Randy Pagels explains how to use GitHub Copilot’s multi-file edits feature to refactor, update, and modernize your codebase efficiently—all while retaining developer control.

Randy Pagels explains how GitHub Copilot helps developers quickly draft, explain, and test regex patterns, making regular expression work less frustrating and more productive.

Randy Pagels explains how developers can leverage GitHub Copilot's Edit Mode for fast, targeted improvements to code bases, demonstrating features such as simplifying logic, matching styles, and automating error handling using AI-driven prompts.

Randy Pagels explains how to leverage GitHub Copilot’s multi-file context abilities to connect logic, refactor code, and reason across entire projects for smarter development.

Randy Pagels demonstrates how GitHub Copilot helps developers speed up API integration, offering practical tips for combining AI code suggestions with their API knowledge.