Improvements in LLM-Assisted Coding Through Customization and Agentic IDEs

Aug 23, 2026

AI Summary

A developer shares their experience using large language models (LLMs) for coding, noting significant improvements in code quality when utilizing agentic IDEs and a customized agent.md file. The developer emphasizes the importance of fine-tuning coding style preferences to enhance productivity and maintainability.

Initial attempts to use LLMs for coding in 2025 resulted in non-compiling code.
A subsequent attempt in January 2026 yielded better results, including the creation of a complex data structure and bug identification, but the code quality was poor.
By March 2026, the developer used agentic IDEs, which allowed for iterative code reviews, leading to improved code quality that was closer to their manual coding standards.
The developer created an agent.md file to specify coding style preferences, reducing repetitive suggestions during coding sessions.
Key rules in the agent.md include minimizing word usage in comments, avoiding magic numbers, maintaining short function names, and adhering to strict coding practices.
The developer notes that while LLMs have improved code generation, they still require thorough review and verification due to potential inaccuracies.
The phenomenon of context dilution in LLMs affects their adherence to instructions, prompting the developer to update agent.md directly for efficiency.

llmcode qualitysoftware developmentai toolsprogramming

Improvements in LLM-Assisted Coding Through Customization and Agentic IDEs

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