Blog Posts & Articles
Overview
This page curates blog posts, articles, and written guides from the Business Central community about using AI coding assistants for AL development.
Featured Articles
Getting Started
“AI for BC Development - The Knowledge Gap That Ships to Production” — Kine
Author: Kine (blog.kine.cz) Published: 2025 Summary: A critical self-assessment guide on whether you are ready to validate what AI produces for BC development. Covers common AI mistakes in AL code (wrong field assignment, missing keys, reinventing standard libraries) and what BC knowledge areas you need to review AI output responsibly.
Key Takeaways:
- Common AI mistakes in AL code that ship to production
- Self-assessment framework for AI-assisted development readiness
- BC knowledge areas required to review AI output responsibly
- Practical examples of wrong field assignments, missing keys, and reinvented standard libraries
Link: https://blog.kine.cz/posts/bcdevelopmentserie-02b/
“Vibe Coding — yes or no?” — Demiliani
Author: Demiliani Published: 2025-08-21 Summary: A thoughtful exploration of the Vibe Coding approach and whether its rules and conventions help or hinder real-world AL development.
Key Takeaways:
- Pros and cons of strict Vibe Coding rules
- When to adopt vs. adapt guidance for your team
- Practical examples and trade-offs
Link: https://demiliani.com/2025/08/21/vibe-coding-yes-or-no/
“Gestión de contexto y estados en servidores MCP” — TechSphere Dynamics
Author: TechSphere Dynamics Published: 2025-08-15 Summary: A Spanish-language deep dive into context handling and state management patterns for MCP servers, including practical patterns used in production systems.
Key Takeaways:
- Context lifecycle and state management strategies
- Common pitfalls when preserving or discarding context
- Examples of robust MCP server patterns
Link: https://techspheredynamics.com/2025/08/15/gestion-de-contexto-y-estados-en-servidores-mcp/
“Testing GitHub Copilot: knowledge engineering — what actually works and what doesn’t” — Nubimancy
Author: Nubimancy Published: 2025-09-09 Summary: An empirical look at how well GitHub Copilot handles knowledge-engineering tasks, with experiments and practical recommendations for prompt authors.
Key Takeaways:
- Which prompting patterns produce reliable results
- When Copilot is prone to hallucination or brittle outputs
- Strategies to validate and refine AI-suggested knowledge artifacts
Each of these are just great examples from those blogs, so make sure to explore around!
Contributing Articles
Have you written about AI-assisted AL development? We’d love to include high-quality, publicly accessible content from the community.
Submission checklist:
- Publicly accessible article or blog post
- Clearly focused on AL / Business Central development
- Accurate, well-written, and actionable
- Original work or properly attributed
How to submit:
- Review the Contributing guidelines
- Open a pull request adding your article to this list
- Provide: Title, Author, Short Summary (1–2 lines), and Link
Article Quality Criteria
Items listed on this page should meet these standards:
- Technically accurate
- Relevant to AL development
- Clear and well-written
- Provides actionable insights
- Publicly accessible
Updates
This is a living document — new resources will be added as the community publishes them. To suggest an addition, open a PR against this repository and follow the submission checklist above.
Related Resources
- Video Tutorials - Visual learning content
- Tools & Extensions - Development utilities
- Discussions - Community conversations
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