Hello everyone,
I’m excited to share a new article exploring how providing full project context to LLMs transforms the AI-assisted development experience.
The core idea: What if AI already understood your entire codebase before you started explaining? No more explaining project structure. No more endless copying of code snippets.
Many of you who use repository packing tools are already experiencing this “full context magic” as your daily norm - the AI assistant truly understands your project without constant explanation. However, I haven’t seen anyone fully articulate this experience in writing or explore its implications for the development workflow.
In Full Context Magic, we demonstrate through real examples how this approach shifts the development workflow from constant explanation to immediate problem-solving.
The article includes:
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Practical examples of bug fixing, feature development, and API integration
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How to set up full context using llm-context with various LLM interfaces
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Comparisons of the traditional vs. full-context approach to AI collaboration
This isn’t about a marginal improvement - it’s about a fundamentally different way to work with AI coding assistants that lets you focus on problems rather than explaining your codebase.
If you’ve been using repository packers or if you’ve been frustrated by the limitations of traditional AI chat interfaces for development tasks, I’d love to hear your thoughts on this approach.